{"schemaVersion":"jobsearcher.job.v1","id":"052d1a1beb50138983e9313e","url":"https://jobsearcher.com/jobs/052d1a1beb50138983e9313e","canonicalUrl":"https://jobsearcher.com/jobs/052d1a1beb50138983e9313e","title":"Data Scientist 3","description":"Position Overview\n\nBLOC Resources is seeking an experienced Data Scientist 3 – Nuclear Operations to support Southern Company and Southern Nuclear's Nuclear Technology Solutions organization.\n\nThis senior-level position is responsible for developing, validating, deploying, and maintaining advanced analytical and machine learning solutions that support nuclear operational performance, equipment reliability, predictive maintenance, and economic outcomes.\n\nThe Data Scientist will transform complex plant telemetry and operational data into actionable insights delivered through a secure, governed, cloud-native analytics environment. A major focus of this position is the production-grade implementation of analytics solutions within the Southern Nuclear Azure Databricks Lakehouse.\n\nBecause this work supports a highly regulated nuclear environment, the successful candidate must demonstrate exceptional attention to accuracy, model validation, documentation, data governance, security, traceability, auditability, and formal change management.\n\nThe ideal candidate will have approximately 10–15 years of data science and advanced analytics experience, with significant experience developing and deploying production machine learning solutions using Python, SQL, Spark, cloud technologies, and large-scale datasets.\n\nKey Responsibilities\nAdvanced Analytics & Machine Learning\nDevelop, test, validate, deploy, and maintain advanced statistical and machine learning models.\nDevelop analytical solutions supporting nuclear operational performance, equipment reliability, predictive maintenance, anomaly detection, and related operational use cases.\nAnalyze large, complex datasets to identify patterns, trends, relationships, risks, and actionable insights.\nApply advanced statistical and machine learning techniques to solve complex operational and business problems.\nDevelop predictive models using appropriate machine learning techniques, including ensemble methods, neural networks, deep learning, and other advanced approaches where appropriate.\nDevelop and optimize algorithms to address complex analytical challenges.\nLead feature engineering activities to identify and develop meaningful variables that improve model performance.\nFormulate and test hypotheses using rigorous statistical methods.\nDesign analytical experiments and, where appropriate, evaluate results using structured experimentation or A/B testing methodologies.\nEnsure analytical approaches are appropriate for the intended operational application.\nModel Validation & Reliability\nApply rigorous validation techniques to ensure analytical models are accurate, reliable, explainable, and suitable for operational decision support.\nDocument model assumptions, limitations, intended uses, validation results, and operational considerations.\nEvaluate model performance against established requirements and success criteria.\nMonitor deployed models over time for changes in performance.\nIdentify model drift, data-quality issues, and changes in underlying operational conditions.\nSupport controlled model updates as data and operating conditions evolve.\nEnsure model outputs can be traced to approved source data.\nPromote reproducibility and repeatability throughout the analytical lifecycle.\nAzure Databricks Lakehouse & Cloud Analytics\nDesign and implement end-to-end analytics workflows within the Southern Nuclear Azure Databricks Lakehouse environment.\nBuild, maintain, and optimize analytical data pipelines.\nDevelop feature datasets supporting machine learning and advanced analytics.\nWork within enterprise medallion architecture standards and established data-management practices.\nLeverage Apache Spark and distributed computing technologies to process large and complex operational datasets.\nIntegrate data from approved enterprise data sources, data lakes, warehouses, and operational systems.\nDevelop scalable analytical solutions capable of supporting production workloads.\nCollaborate with data engineering and platform teams on data availability, architecture, integration, and performance.\nApply approved experiment tracking and model-lifecycle practices to support governance and repeatability.\nMLOps & Production Model Deployment\nSupport the development and implementation of repeatable machine learning training and deployment workflows.\nDeploy analytical and machine learning models into controlled production environments.\nParticipate in the development and improvement of MLOps practices.\nEstablish repeatable processes for model training, testing, validation, deployment, monitoring, and controlled updates.\nEnsure production models are maintainable, traceable, and appropriately documented.\nCoordinate with platform, engineering, and IT teams to ensure successful model integration.\nSupport production analytics used for operational and business decision-making.\nTime-Series & Nuclear Operational Analytics\nAnalyze industrial and operational time-series datasets.\nDevelop models capable of identifying abnormal equipment or system behavior.\nApply predictive analytics to support equipment reliability and predictive maintenance.\nEvaluate historical and real-time operational data to identify meaningful performance patterns.\nWork with nuclear engineers and operational subject-matter experts to understand plant telemetry and operational context.\nTranslate engineering and operational questions into measurable analytical problems.\nEnsure statistical conclusions appropriately reflect the characteristics and limitations of the underlying data.\nApplied AI & Advanced Analytics\nSupport approved AI-enabled analytical and search capabilities.\nEvaluate retrieval-based and other advanced AI techniques when appropriate for approved use cases.\nEnsure AI-assisted solutions are transparent, verifiable, traceable, and aligned with enterprise and regulatory expectations.\nEvaluate emerging data science and AI technologies for potential business and operational applications.\nPromote responsible and controlled implementation of advanced AI capabilities.\nEnsure AI-enabled solutions operate within established security, data-governance, and change-management requirements.\nData Governance, Security & Compliance\nDesign analytical solutions in accordance with enterprise data-governance standards.\nMaintain appropriate data access controls and security requirements.\nEnsure analytical outputs are traceable to approved data sources and inputs.\nFollow established data-handling and information-security standards.\nOperate within platform security controls designed to limit unauthorized data ingress, egress, and access.\nSupport auditability of data science models, analytical processes, and outputs.\nApply appropriate data privacy, ethical data-use, and compliance practices.\nEnsure analytical solutions meet Southern Company requirements for quality, accuracy, documentation, and auditability.\nWork in accordance with nuclear safety culture and formal change-management practices.\nData Visualization & Communication\nDevelop clear and effective visualizations that communicate complex analytical findings.\nPresent analytical insights to technical and non-technical stakeholders.\nUse tools such as Power BI, Tableau, Python visualization libraries, or other approved enterprise tools as appropriate.\nTranslate highly technical statistical and machine learning results into understandable business and operational information.\nClearly communicate model assumptions, methodologies, limitations, risks, and conclusions.\nDevelop presentations and supporting documentation for engineering, technology, analytics, and leadership stakeholders.\nCross-Functional Collaboration\nPartner closely with nuclear engineers, IT leaders, data engineers, platform teams, business analysts, domain experts, and analytics professionals.\nParticipate in technical design, architecture, and solution-development discussions.\nTranslate operational and engineering requirements into appropriate analytical approaches.\nCollaborate with IT and data engineering teams to integrate approved data sources.\nCoordinate with stakeholders throughout model development, validation, deployment, and operational support.\nBuild effective relationships across technical and operational organizations.\nServe as a senior data science resource on complex analytical initiatives.\nTechnical Leadership & Mentorship\n\nAs a senior-level Data Scientist, the successful candidate may also:\n\nProvide technical guidance and mentorship to junior data scientists and analytics professionals.\nReview analytical methodologies, model designs, and technical solutions.\nPromote data science standards and best practices.\nHelp improve organizational capabilities in machine learning, advanced analytics, MLOps, and AI.\nContribute to longer-term analytics and data science strategy.\nEvaluate new technologies, techniques, frameworks, and tools.\nEncourage innovation while maintaining appropriate governance and operational controls.\nRequired Qualifications\nApproximately 10–15 years of professional data science, advanced analytics, machine learning, or closely related experience.\nDemonstrated history of developing and implementing production-grade data science solutions.\nAdvanced proficiency in Python for data analysis, statistical modeling, and machine learning.\nStrong proficiency in SQL.\nExperience working with large-scale structured datasets.\nStrong understanding of statistical modeling and statistical validation.\nStrong experience with time-series analysis.\nExperience developing and deploying machine learning models in production cloud environments.\nExperience with big-data processing and distributed computing technologies.\nExperience with Apache Spark or similar large-scale processing frameworks.\nStrong understanding of machine learning algorithms and their practical applications.\nExperience integrating data from multiple enterprise sources.\nStrong analytical and critical-thinking capabilities.\nExcellent technical documentation skills.\nAbility to clearly explain analytical methodologies, assumptions, limitations, and results.\nStrong written and verbal communication skills.\nAbility to collaborate effectively with technical, operational, and business stakeholders.\nEducation\n\nMaster's degree preferred in one of the following or another related quantitative discipline:\n\nData Science\nComputer Science\nStatistics\nMathematics\nEngineering\nPhysics\nApplied Mathematics\nArtificial Intelligence/Machine Learning\n\nA Ph.D. in a relevant quantitative discipline may also be highly desirable depending on the candidate's professional experience.\n\nTechnical Skills\n\nStrong candidates should demonstrate experience with several of the following:\n\nPython\nSQL\nAzure Databricks\nApache Spark\nCloud-based analytics\nMachine learning\nStatistical modeling\nTime-series analysis\nPredictive modeling\nPredictive maintenance\nAnomaly detection\nFeature engineering\nDeep learning\nNeural networks\nEnsemble methods\nModel validation\nModel monitoring\nMLOps\nExperiment tracking\nData pipelines\nData lakes\nData warehouses\nMedallion architecture\nData integration\nData visualization\nPower BI\nTableau\nPython visualization libraries\nApplied AI\nRetrieval-based AI techniques\nEnterprise data governance\nAccess control\nModel lifecycle management\nPreferred Qualifications\nExperience supporting nuclear energy, utilities, power generation, energy, industrial operations, or another highly regulated industry.\nExperience with nuclear or industrial plant telemetry.\nExperience analyzing industrial or operational time-series data.\nAzure Databricks Lakehouse experience.\nExperience implementing scalable Spark-based analytics.\nExperience with enterprise medallion architecture.\nExperience developing predictive-maintenance or equipment-reliability models.\nExperience with anomaly-detection applications.\nMLOps experience, including repeatable model training and deployment workflows.\nExperience with enterprise data-governance frameworks.\nUnderstanding of role-based access controls and secure data environments.\nExperience working under formal change-management processes.\nExperience developing analytical solutions subject to audit or regulatory review.\nExperience implementing governed AI or advanced analytics capabilities.\nDocumentation & Quality Standards\n\nDocumentation is a critical component of this position. The Data Scientist will be expected to produce comprehensive technical documentation covering:\n\nAnalytical objectives and intended use\nSource data and approved inputs\nData preparation and transformation\nFeature engineering\nModel methodology\nAssumptions\nStatistical validation\nModel-performance results\nLimitations and risks\nDeployment methodology\nOperational considerations\nMonitoring requirements\nModel changes and updates\n\nAll work must support appropriate quality, accuracy, repeatability, traceability, maintainability, and auditability.\n\nKnowledge & Competencies\n\nThe successful candidate should demonstrate:\n\nExceptional analytical rigor and attention to detail.\nAdvanced problem-solving and critical-thinking abilities.\nAbility to work with complex and highly technical datasets.\nStrong understanding of statistical and machine learning principles.\nAbility to balance innovation with security, governance, and operational controls.\nStrong technical leadership capabilities.\nAbility to communicate effectively with engineers, technology professionals, analysts, and senior stakeholders.\nStrong organizational and project-management skills.\nAbility to independently manage complex analytical assignments.\nAbility to work effectively in a multidisciplinary environment.\nCommitment to ethical and responsible use of data and artificial intelligence.\nBehavioral Attributes\n\nThe successful candidate is expected to demonstrate Southern Company's core values and professional expectations, including:\n\nSafety First\nAct with Integrity\nIntentional Inclusion\nSuperior Performance\n\nCandidates should also demonstrate professionalism, accountability, collaboration, technical curiosity, attention to detail, and the ability to operate successfully in an environment with formal controls and regulatory oversight.\n\nNuclear & Regulatory Environment\n\nBecause this position supports Southern Nuclear operations, the selected candidate must be comfortable working in a highly regulated nuclear environment where data accuracy, cybersecurity, documentation, quality assurance, traceability, and formal change control are critical.\n\nThe position may require compliance with applicable nuclear regulatory and company requirements, including background screening, testing, training, security requirements, and other qualification processes, as required by policy.\n\nWork Location\n\n260 Southfield Parkway\nForest Park, GA 30297\n\nOn-site and work-location requirements will be aligned with Southern Company's business and operational needs.\n\nData Scientist 3 – Nuclear Operations\n\nCompany: BLOC Resources\nClient: Southern Company / Southern Nuclear\nLocation: 260 Southfield Parkway, Forest Park, GA 30297\nPay Rate: $50.00–$56.00 per hour, based on experience and qualifications\nPosition Type: Contract / Supplemental Workforce\nIndustry: Nuclear Energy / Data Science / Advanced Analytics / Artificial Intelligence\n\nPosition Overview\n\nBLOC Resources is seeking an experienced Data Scientist 3 – Nuclear Operations to support Southern Company and Southern Nuclear's Nuclear Technology Solutions organization.\n\nThis senior-level position is responsible for developing, validating, deploying, and maintaining advanced analytical and machine learning solutions that support nuclear operational performance, equipment reliability, predictive maintenance, and economic outcomes.\n\nThe Data Scientist will transform complex plant telemetry and operational data into actionable insights delivered through a secure, governed, cloud-native analytics environment. A major focus of this position is the production-grade implementation of analytics solutions within the Southern Nuclear Azure Databricks Lakehouse.\n\nBecause this work supports a highly regulated nuclear environment, the successful candidate must demonstrate exceptional attention to accuracy, model validation, documentation, data governance, security, traceability, auditability, and formal change management.\n\nThe ideal candidate will have approximately 10–15 years of data science and advanced analytics experience, with significant experience developing and deploying production machine learning solutions using Python, SQL, Spark, cloud technologies, and large-scale datasets.\n\nKey Responsibilities\nAdvanced Analytics & Machine Learning\n\nDevelop, test, validate, deploy, and maintain advanced statistical and machine learning models.\n\nDevelop analytical solutions supporting nuclear operational performance, equipment reliability, predictive maintenance, anomaly detection, and related operational use cases.\n\nAnalyze large, complex datasets to identify patterns, trends, relationships, risks, and actionable insights.\n\nApply advanced statistical and machine learning techniques to solve complex operational and business problems.\n\nDevelop predictive models using appropriate machine learning techniques, including ensemble methods, neural networks, deep learning, and other advanced approaches where appropriate.\n\nDevelop and optimize algorithms to address complex analytical challenges.\n\nLead feature engineering activities to identify and develop meaningful variables that improve model performance.\n\nFormulate and test hypotheses using rigorous statistical methods.\n\nDesign analytical experiments and, where appropriate, evaluate results using structured experimentation or A/B testing methodologies.\n\nEnsure analytical approaches are appropriate for the intended operational application.\n\nModel Validation & Reliability\n\nApply rigorous validation techniques to ensure analytical models are accurate, reliable, explainable, and suitable for operational decision support.\n\nDocument model assumptions, limitations, intended uses, validation results, and operational considerations.\n\nEvaluate model performance against established requirements and success criteria.\n\nMonitor deployed models over time for changes in performance.\n\nIdentify model drift, data-quality issues, and changes in underlying operational conditions.\n\nSupport controlled model updates as data and operating conditions evolve.\n\nEnsure model outputs can be traced to approved source data.\n\nPromote reproducibility and repeatability throughout the analytical lifecycle.\n\nAzure Databricks Lakehouse & Cloud Analytics\n\nDesign and implement end-to-end analytics workflows within the Southern Nuclear Azure Databricks Lakehouse environment.\n\nBuild, maintain, and optimize analytical data pipelines.\n\nDevelop feature datasets supporting machine learning and advanced analytics.\n\nWork within enterprise medallion architecture standards and established data-management practices.\n\nLeverage Apache Spark and distributed computing technologies to process large and complex operational datasets.\n\nIntegrate data from approved enterprise data sources, data lakes, warehouses, and operational systems.\n\nDevelop scalable analytical solutions capable of supporting production workloads.\n\nCollaborate with data engineering and platform teams on data availability, architecture, integration, and performance.\n\nApply approved experiment tracking and model-lifecycle practices to support governance and repeatability.\n\nMLOps & Production Model Deployment\n\nSupport the development and implementation of repeatable machine learning training and deployment workflows.\n\nDeploy analytical and machine learning models into controlled production environments.\n\nParticipate in the development and improvement of MLOps practices.\n\nEstablish repeatable processes for model training, testing, validation, deployment, monitoring, and controlled updates.\n\nEnsure production models are maintainable, traceable, and appropriately documented.\n\nCoordinate with platform, engineering, and IT teams to ensure successful model integration.\n\nSupport production analytics used for operational and business decision-making.\n\nTime-Series & Nuclear Operational Analytics\n\nAnalyze industrial and operational time-series datasets.\n\nDevelop models capable of identifying abnormal equipment or system behavior.\n\nApply predictive analytics to support equipment reliability and predictive maintenance.\n\nEvaluate historical and real-time operational data to identify meaningful performance patterns.\n\nWork with nuclear engineers and operational subject-matter experts to understand plant telemetry and operational context.\n\nTranslate engineering and operational questions into measurable analytical problems.\n\nEnsure statistical conclusions appropriately reflect the characteristics and limitations of the underlying data.\n\nApplied AI & Advanced Analytics\n\nSupport approved AI-enabled analytical and search capabilities.\n\nEvaluate retrieval-based and other advanced AI techniques when appropriate for approved use cases.\n\nEnsure AI-assisted solutions are transparent, verifiable, traceable, and aligned with enterprise and regulatory expectations.\n\nEvaluate emerging data science and AI technologies for potential business and operational applications.\n\nPromote responsible and controlled implementation of advanced AI capabilities.\n\nEnsure AI-enabled solutions operate within established security, data-governance, and change-management requirements.\n\nData Governance, Security & Compliance\n\nDesign analytical solutions in accordance with enterprise data-governance standards.\n\nMaintain appropriate data access controls and security requirements.\n\nEnsure analytical outputs are traceable to approved data sources and inputs.\n\nFollow established data-handling and information-security standards.\n\nOperate within platform security controls designed to limit unauthorized data ingress, egress, and access.\n\nSupport auditability of data science models, analytical processes, and outputs.\n\nApply appropriate data privacy, ethical data-use, and compliance practices.\n\nEnsure analytical solutions meet Southern Company requirements for quality, accuracy, documentation, and auditability.\n\nWork in accordance with nuclear safety culture and formal change-management practices.\n\nData Visualization & Communication\n\nDevelop clear and effective visualizations that communicate complex analytical findings.\n\nPresent analytical insights to technical and non-technical stakeholders.\n\nUse tools such as Power BI, Tableau, Python visualization libraries, or other approved enterprise tools as appropriate.\n\nTranslate highly technical statistical and machine learning results into understandable business and operational information.\n\nClearly communicate model assumptions, methodologies, limitations, risks, and conclusions.\n\nDevelop presentations and supporting documentation for engineering, technology, analytics, and leadership stakeholders.\n\nCross-Functional Collaboration\n\nPartner closely with nuclear engineers, IT leaders, data engineers, platform teams, business analysts, domain experts, and analytics professionals.\n\nParticipate in technical design, architecture, and solution-development discussions.\n\nTranslate operational and engineering requirements into appropriate analytical approaches.\n\nCollaborate with IT and data engineering teams to integrate approved data sources.\n\nCoordinate with stakeholders throughout model development, validation, deployment, and operational support.\n\nBuild effective relationships across technical and operational organizations.\n\nServe as a senior data science resource on complex analytical initiatives.\n\nTechnical Leadership & Mentorship\n\nAs a senior-level Data Scientist, the successful candidate may also:\n\nProvide technical guidance and mentorship to junior data scientists and analytics professionals.\n\nReview analytical methodologies, model designs, and technical solutions.\n\nPromote data science standards and best practices.\n\nHelp improve organizational capabilities in machine learning, advanced analytics, MLOps, and AI.\n\nContribute to longer-term analytics and data science strategy.\n\nEvaluate new technologies, techniques, frameworks, and tools.\n\nEncourage innovation while maintaining appropriate governance and operational controls.\n\nRequired Qualifications\n\nApproximately 10–15 years of professional data science, advanced analytics, machine learning, or closely related experience.\n\nDemonstrated history of developing and implementing production-grade data science solutions.\n\nAdvanced proficiency in Python for data analysis, statistical modeling, and machine learning.\n\nStrong proficiency in SQL.\n\nExperience working with large-scale structured datasets.\n\nStrong understanding of statistical modeling and statistical validation.\n\nStrong experience with time-series analysis.\n\nExperience developing and deploying machine learning models in production cloud environments.\n\nExperience with big-data processing and distributed computing technologies.\n\nExperience with Apache Spark or similar large-scale processing frameworks.\n\nStrong understanding of machine learning algorithms and their practical applications.\n\nExperience integrating data from multiple enterprise sources.\n\nStrong analytical and critical-thinking capabilities.\n\nExcellent technical documentation skills.\n\nAbility to clearly explain analytical methodologies, assumptions, limitations, and results.\n\nStrong written and verbal communication skills.\n\nAbility to collaborate effectively with technical, operational, and business stakeholders.\n\nEducation\n\nMaster's degree preferred in one of the following or another related quantitative discipline:\n\nData Science\n\nComputer Science\n\nStatistics\n\nMathematics\n\nEngineering\n\nPhysics\n\nApplied Mathematics\n\nArtificial Intelligence/Machine Learning\n\nA Ph.D. in a relevant quantitative discipline may also be highly desirable depending on the candidate's professional experience.\n\nTechnical Skills\n\nStrong candidates should demonstrate experience with several of the following:\n\nPython\n\nSQL\n\nAzure Databricks\n\nApache Spark\n\nCloud-based analytics\n\nMachine learning\n\nStatistical modeling\n\nTime-series analysis\n\nPredictive modeling\n\nPredictive maintenance\n\nAnomaly detection\n\nFeature engineering\n\nDeep learning\n\nNeural networks\n\nEnsemble methods\n\nModel validation\n\nModel monitoring\n\nMLOps\n\nExperiment tracking\n\nData pipelines\n\nData lakes\n\nData warehouses\n\nMedallion architecture\n\nData integration\n\nData visualization\n\nPower BI\n\nTableau\n\nPython visualization libraries\n\nApplied AI\n\nRetrieval-based AI techniques\n\nEnterprise data governance\n\nAccess control\n\nModel lifecycle management\n\nPreferred Qualifications\n\nExperience supporting nuclear energy, utilities, power generation, energy, industrial operations, or another highly regulated industry.\n\nExperience with nuclear or industrial plant telemetry.\n\nExperience analyzing industrial or operational time-series data.\n\nAzure Databricks Lakehouse experience.\n\nExperience implementing scalable Spark-based analytics.\n\nExperience with enterprise medallion architecture.\n\nExperience developing predictive-maintenance or equipment-reliability models.\n\nExperience with anomaly-detection applications.\n\nMLOps experience, including repeatable model training and deployment workflows.\n\nExperience with enterprise data-governance frameworks.\n\nUnderstanding of role-based access controls and secure data environments.\n\nExperience working under formal change-management processes.\n\nExperience developing analytical solutions subject to audit or regulatory review.\n\nExperience implementing governed AI or advanced analytics capabilities.\n\nDocumentation & Quality Standards\n\nDocumentation is a critical component of this position. The Data Scientist will be expected to produce comprehensive technical documentation covering:\n\nAnalytical objectives and intended use\n\nSource data and approved inputs\n\nData preparation and transformation\n\nFeature engineering\n\nModel methodology\n\nAssumptions\n\nStatistical validation\n\nModel-performance results\n\nLimitations and risks\n\nDeployment methodology\n\nOperational considerations\n\nMonitoring requirements\n\nModel changes and updates\n\nAll work must support appropriate quality, accuracy, repeatability, traceability, maintainability, and auditability.\n\nKnowledge & Competencies\n\nThe successful candidate should demonstrate:\n\nExceptional analytical rigor and attention to detail.\n\nAdvanced problem-solving and critical-thinking abilities.\n\nAbility to work with complex and highly technical datasets.\n\nStrong understanding of statistical and machine learning principles.\n\nAbility to balance innovation with security, governance, and operational controls.\n\nStrong technical leadership capabilities.\n\nAbility to communicate effectively with engineers, technology professionals, analysts, and senior stakeholders.\n\nStrong organizational and project-management skills.\n\nAbility to independently manage complex analytical assignments.\n\nAbility to work effectively in a multidisciplinary environment.\n\nCommitment to ethical and responsible use of data and artificial intelligence.\n\nBehavioral Attributes\n\nThe successful candidate is expected to demonstrate Southern Company's core values and professional expectations, including:\n\nSafety First\n\nAct with Integrity\n\nIntentional Inclusion\n\nSuperior Performance\n\nCandidates should also demonstrate professionalism, accountability, collaboration, technical curiosity, attention to detail, and the ability to operate successfully in an environment with formal controls and regulatory oversight.\n\nNuclear & Regulatory Environment\n\nBecause this position supports Southern Nuclear operations, the selected candidate must be comfortable working in a highly regulated nuclear environment where data accuracy, cybersecurity, documentation, quality assurance, traceability, and formal change control are critical.\n\nThe position may require compliance with applicable nuclear regulatory and company requirements, including background screening, testing, training, security requirements, and other qualification processes, as required by policy.\n\nWork Location\n\n260 Southfield Parkway\nForest Park, GA 30297\n\nOn-site and work-location requirements will be aligned with Southern Company's business and operational needs.\n\nCompensation\n\n$50.00–$56.00 per hour, based on experience and qualifications.\n\nFinal compensation will consider the candidate's overall data science experience, Azure Databricks and cloud expertise, machine learning background, production deployment experience, nuclear/utility industry experience, and other relevant qualifications.\n\nWhy Join BLOC Resources?\n\nThis opportunity provides an experienced Data Scientist the ability to:\n\nSupport advanced analytics within the nuclear power industry.\n\nWork with complex nuclear operational and equipment data.\n\nDevelop production-grade machine learning solutions.\n\nWork within an enterprise Azure Databricks Lakehouse environment.\n\nContribute to predictive maintenance, equipment reliability, anomaly detection, and operational-performance initiatives.\n\nCollaborate directly with nuclear engineers, technology professionals, and analytics teams.\n\nGain exposure to governed AI and advanced analytics in a highly regulated environment.\n\nContribute to the modernization of data-driven nuclear operations.\n\nReceive ongoing recruiting and workforce support from BLOC Resources.\n\nIdeal Candidate Profile\n\nThe strongest candidate will be a senior Data Scientist with approximately 10–15 years of experience who combines advanced machine learning and statistical expertise with hands-on production implementation capabilities.\n\nThe candidate should be highly proficient in Python, SQL, Spark, cloud analytics, machine learning, time-series modeling, model validation, and production deployment.\n\nExperience with Azure Databricks, MLOps, industrial time-series data, predictive maintenance, equipment reliability, nuclear energy, utilities, or another highly regulated environment would make a candidate particularly competitive.\n\nMost importantly, this individual must understand that success in a nuclear environment requires more than building a high-performing model—the solution must also be explainable, controlled, documented, secure, repeatable, traceable, and auditable.\n\nEqual Employment Opportunity\n\nBLOC Resources is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment consistent with applicable federal, state, and local requirements.","company":"Bloc Resources","rawCompany":"bloc resources","city":"Forest Park","state":"GA","isRemote":false,"isActive":false,"createdAt":"2026-09-14T09:56:40.780Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2041.00","title":"Statisticians","slug":"statisticians"}],"industries":[{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"221113","title":"Nuclear Electric Power Generation","slug":"nuclear-electric-power-generation"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Scientist 3","description":"Position Overview\n\nBLOC Resources is seeking an experienced Data Scientist 3 – Nuclear Operations to support Southern Company and Southern Nuclear's Nuclear Technology Solutions organization.\n\nThis senior-level position is responsible for developing, validating, deploying, and maintaining advanced analytical and machine learning solutions that support nuclear operational performance, equipment reliability, predictive maintenance, and economic outcomes.\n\nThe Data Scientist will transform complex plant telemetry and operational data into actionable insights delivered through a secure, governed, cloud-native analytics environment. A major focus of this position is the production-grade implementation of analytics solutions within the Southern Nuclear Azure Databricks Lakehouse.\n\nBecause this work supports a highly regulated nuclear environment, the successful candidate must demonstrate exceptional attention to accuracy, model validation, documentation, data governance, security, traceability, auditability, and formal change management.\n\nThe ideal candidate will have approximately 10–15 years of data science and advanced analytics experience, with significant experience developing and deploying production machine learning solutions using Python, SQL, Spark, cloud technologies, and large-scale datasets.\n\nKey Responsibilities\nAdvanced Analytics & Machine Learning\nDevelop, test, validate, deploy, and maintain advanced statistical and machine learning models.\nDevelop analytical solutions supporting nuclear operational performance, equipment reliability, predictive maintenance, anomaly detection, and related operational use cases.\nAnalyze large, complex datasets to identify patterns, trends, relationships, risks, and actionable insights.\nApply advanced statistical and machine learning techniques to solve complex operational and business problems.\nDevelop predictive models using appropriate machine learning techniques, including ensemble methods, neural networks, deep learning, and other advanced approaches where appropriate.\nDevelop and optimize algorithms to address complex analytical challenges.\nLead feature engineering activities to identify and develop meaningful variables that improve model performance.\nFormulate and test hypotheses using rigorous statistical methods.\nDesign analytical experiments and, where appropriate, evaluate results using structured experimentation or A/B testing methodologies.\nEnsure analytical approaches are appropriate for the intended operational application.\nModel Validation & Reliability\nApply rigorous validation techniques to ensure analytical models are accurate, reliable, explainable, and suitable for operational decision support.\nDocument model assumptions, limitations, intended uses, validation results, and operational considerations.\nEvaluate model performance against established requirements and success criteria.\nMonitor deployed models over time for changes in performance.\nIdentify model drift, data-quality issues, and changes in underlying operational conditions.\nSupport controlled model updates as data and operating conditions evolve.\nEnsure model outputs can be traced to approved source data.\nPromote reproducibility and repeatability throughout the analytical lifecycle.\nAzure Databricks Lakehouse & Cloud Analytics\nDesign and implement end-to-end analytics workflows within the Southern Nuclear Azure Databricks Lakehouse environment.\nBuild, maintain, and optimize analytical data pipelines.\nDevelop feature datasets supporting machine learning and advanced analytics.\nWork within enterprise medallion architecture standards and established data-management practices.\nLeverage Apache Spark and distributed computing technologies to process large and complex operational datasets.\nIntegrate data from approved enterprise data sources, data lakes, warehouses, and operational systems.\nDevelop scalable analytical solutions capable of supporting production workloads.\nCollaborate with data engineering and platform teams on data availability, architecture, integration, and performance.\nApply approved experiment tracking and model-lifecycle practices to support governance and repeatability.\nMLOps & Production Model Deployment\nSupport the development and implementation of repeatable machine learning training and deployment workflows.\nDeploy analytical and machine learning models into controlled production environments.\nParticipate in the development and improvement of MLOps practices.\nEstablish repeatable processes for model training, testing, validation, deployment, monitoring, and controlled updates.\nEnsure production models are maintainable, traceable, and appropriately documented.\nCoordinate with platform, engineering, and IT teams to ensure successful model integration.\nSupport production analytics used for operational and business decision-making.\nTime-Series & Nuclear Operational Analytics\nAnalyze industrial and operational time-series datasets.\nDevelop models capable of identifying abnormal equipment or system behavior.\nApply predictive analytics to support equipment reliability and predictive maintenance.\nEvaluate historical and real-time operational data to identify meaningful performance patterns.\nWork with nuclear engineers and operational subject-matter experts to understand plant telemetry and operational context.\nTranslate engineering and operational questions into measurable analytical problems.\nEnsure statistical conclusions appropriately reflect the characteristics and limitations of the underlying data.\nApplied AI & Advanced Analytics\nSupport approved AI-enabled analytical and search capabilities.\nEvaluate retrieval-based and other advanced AI techniques when appropriate for approved use cases.\nEnsure AI-assisted solutions are transparent, verifiable, traceable, and aligned with enterprise and regulatory expectations.\nEvaluate emerging data science and AI technologies for potential business and operational applications.\nPromote responsible and controlled implementation of advanced AI capabilities.\nEnsure AI-enabled solutions operate within established security, data-governance, and change-management requirements.\nData Governance, Security & Compliance\nDesign analytical solutions in accordance with enterprise data-governance standards.\nMaintain appropriate data access controls and security requirements.\nEnsure analytical outputs are traceable to approved data sources and inputs.\nFollow established data-handling and information-security standards.\nOperate within platform security controls designed to limit unauthorized data ingress, egress, and access.\nSupport auditability of data science models, analytical processes, and outputs.\nApply appropriate data privacy, ethical data-use, and compliance practices.\nEnsure analytical solutions meet Southern Company requirements for quality, accuracy, documentation, and auditability.\nWork in accordance with nuclear safety culture and formal change-management practices.\nData Visualization & Communication\nDevelop clear and effective visualizations that communicate complex analytical findings.\nPresent analytical insights to technical and non-technical stakeholders.\nUse tools such as Power BI, Tableau, Python visualization libraries, or other approved enterprise tools as appropriate.\nTranslate highly technical statistical and machine learning results into understandable business and operational information.\nClearly communicate model assumptions, methodologies, limitations, risks, and conclusions.\nDevelop presentations and supporting documentation for engineering, technology, analytics, and leadership stakeholders.\nCross-Functional Collaboration\nPartner closely with nuclear engineers, IT leaders, data engineers, platform teams, business analysts, domain experts, and analytics professionals.\nParticipate in technical design, architecture, and solution-development discussions.\nTranslate operational and engineering requirements into appropriate analytical approaches.\nCollaborate with IT and data engineering teams to integrate approved data sources.\nCoordinate with stakeholders throughout model development, validation, deployment, and operational support.\nBuild effective relationships across technical and operational organizations.\nServe as a senior data science resource on complex analytical initiatives.\nTechnical Leadership & Mentorship\n\nAs a senior-level Data Scientist, the successful candidate may also:\n\nProvide technical guidance and mentorship to junior data scientists and analytics professionals.\nReview analytical methodologies, model designs, and technical solutions.\nPromote data science standards and best practices.\nHelp improve organizational capabilities in machine learning, advanced analytics, MLOps, and AI.\nContribute to longer-term analytics and data science strategy.\nEvaluate new technologies, techniques, frameworks, and tools.\nEncourage innovation while maintaining appropriate governance and operational controls.\nRequired Qualifications\nApproximately 10–15 years of professional data science, advanced analytics, machine learning, or closely related experience.\nDemonstrated history of developing and implementing production-grade data science solutions.\nAdvanced proficiency in Python for data analysis, statistical modeling, and machine learning.\nStrong proficiency in SQL.\nExperience working with large-scale structured datasets.\nStrong understanding of statistical modeling and statistical validation.\nStrong experience with time-series analysis.\nExperience developing and deploying machine learning models in production cloud environments.\nExperience with big-data processing and distributed computing technologies.\nExperience with Apache Spark or similar large-scale processing frameworks.\nStrong understanding of machine learning algorithms and their practical applications.\nExperience integrating data from multiple enterprise sources.\nStrong analytical and critical-thinking capabilities.\nExcellent technical documentation skills.\nAbility to clearly explain analytical methodologies, assumptions, limitations, and results.\nStrong written and verbal communication skills.\nAbility to collaborate effectively with technical, operational, and business stakeholders.\nEducation\n\nMaster's degree preferred in one of the following or another related quantitative discipline:\n\nData Science\nComputer Science\nStatistics\nMathematics\nEngineering\nPhysics\nApplied Mathematics\nArtificial Intelligence/Machine Learning\n\nA Ph.D. in a relevant quantitative discipline may also be highly desirable depending on the candidate's professional experience.\n\nTechnical Skills\n\nStrong candidates should demonstrate experience with several of the following:\n\nPython\nSQL\nAzure Databricks\nApache Spark\nCloud-based analytics\nMachine learning\nStatistical modeling\nTime-series analysis\nPredictive modeling\nPredictive maintenance\nAnomaly detection\nFeature engineering\nDeep learning\nNeural networks\nEnsemble methods\nModel validation\nModel monitoring\nMLOps\nExperiment tracking\nData pipelines\nData lakes\nData warehouses\nMedallion architecture\nData integration\nData visualization\nPower BI\nTableau\nPython visualization libraries\nApplied AI\nRetrieval-based AI techniques\nEnterprise data governance\nAccess control\nModel lifecycle management\nPreferred Qualifications\nExperience supporting nuclear energy, utilities, power generation, energy, industrial operations, or another highly regulated industry.\nExperience with nuclear or industrial plant telemetry.\nExperience analyzing industrial or operational time-series data.\nAzure Databricks Lakehouse experience.\nExperience implementing scalable Spark-based analytics.\nExperience with enterprise medallion architecture.\nExperience developing predictive-maintenance or equipment-reliability models.\nExperience with anomaly-detection applications.\nMLOps experience, including repeatable model training and deployment workflows.\nExperience with enterprise data-governance frameworks.\nUnderstanding of role-based access controls and secure data environments.\nExperience working under formal change-management processes.\nExperience developing analytical solutions subject to audit or regulatory review.\nExperience implementing governed AI or advanced analytics capabilities.\nDocumentation & Quality Standards\n\nDocumentation is a critical component of this position. The Data Scientist will be expected to produce comprehensive technical documentation covering:\n\nAnalytical objectives and intended use\nSource data and approved inputs\nData preparation and transformation\nFeature engineering\nModel methodology\nAssumptions\nStatistical validation\nModel-performance results\nLimitations and risks\nDeployment methodology\nOperational considerations\nMonitoring requirements\nModel changes and updates\n\nAll work must support appropriate quality, accuracy, repeatability, traceability, maintainability, and auditability.\n\nKnowledge & Competencies\n\nThe successful candidate should demonstrate:\n\nExceptional analytical rigor and attention to detail.\nAdvanced problem-solving and critical-thinking abilities.\nAbility to work with complex and highly technical datasets.\nStrong understanding of statistical and machine learning principles.\nAbility to balance innovation with security, governance, and operational controls.\nStrong technical leadership capabilities.\nAbility to communicate effectively with engineers, technology professionals, analysts, and senior stakeholders.\nStrong organizational and project-management skills.\nAbility to independently manage complex analytical assignments.\nAbility to work effectively in a multidisciplinary environment.\nCommitment to ethical and responsible use of data and artificial intelligence.\nBehavioral Attributes\n\nThe successful candidate is expected to demonstrate Southern Company's core values and professional expectations, including:\n\nSafety First\nAct with Integrity\nIntentional Inclusion\nSuperior Performance\n\nCandidates should also demonstrate professionalism, accountability, collaboration, technical curiosity, attention to detail, and the ability to operate successfully in an environment with formal controls and regulatory oversight.\n\nNuclear & Regulatory Environment\n\nBecause this position supports Southern Nuclear operations, the selected candidate must be comfortable working in a highly regulated nuclear environment where data accuracy, cybersecurity, documentation, quality assurance, traceability, and formal change control are critical.\n\nThe position may require compliance with applicable nuclear regulatory and company requirements, including background screening, testing, training, security requirements, and other qualification processes, as required by policy.\n\nWork Location\n\n260 Southfield Parkway\nForest Park, GA 30297\n\nOn-site and work-location requirements will be aligned with Southern Company's business and operational needs.\n\nData Scientist 3 – Nuclear Operations\n\nCompany: BLOC Resources\nClient: Southern Company / Southern Nuclear\nLocation: 260 Southfield Parkway, Forest Park, GA 30297\nPay Rate: $50.00–$56.00 per hour, based on experience and qualifications\nPosition Type: Contract / Supplemental Workforce\nIndustry: Nuclear Energy / Data Science / Advanced Analytics / Artificial Intelligence\n\nPosition Overview\n\nBLOC Resources is seeking an experienced Data Scientist 3 – Nuclear Operations to support Southern Company and Southern Nuclear's Nuclear Technology Solutions organization.\n\nThis senior-level position is responsible for developing, validating, deploying, and maintaining advanced analytical and machine learning solutions that support nuclear operational performance, equipment reliability, predictive maintenance, and economic outcomes.\n\nThe Data Scientist will transform complex plant telemetry and operational data into actionable insights delivered through a secure, governed, cloud-native analytics environment. A major focus of this position is the production-grade implementation of analytics solutions within the Southern Nuclear Azure Databricks Lakehouse.\n\nBecause this work supports a highly regulated nuclear environment, the successful candidate must demonstrate exceptional attention to accuracy, model validation, documentation, data governance, security, traceability, auditability, and formal change management.\n\nThe ideal candidate will have approximately 10–15 years of data science and advanced analytics experience, with significant experience developing and deploying production machine learning solutions using Python, SQL, Spark, cloud technologies, and large-scale datasets.\n\nKey Responsibilities\nAdvanced Analytics & Machine Learning\n\nDevelop, test, validate, deploy, and maintain advanced statistical and machine learning models.\n\nDevelop analytical solutions supporting nuclear operational performance, equipment reliability, predictive maintenance, anomaly detection, and related operational use cases.\n\nAnalyze large, complex datasets to identify patterns, trends, relationships, risks, and actionable insights.\n\nApply advanced statistical and machine learning techniques to solve complex operational and business problems.\n\nDevelop predictive models using appropriate machine learning techniques, including ensemble methods, neural networks, deep learning, and other advanced approaches where appropriate.\n\nDevelop and optimize algorithms to address complex analytical challenges.\n\nLead feature engineering activities to identify and develop meaningful variables that improve model performance.\n\nFormulate and test hypotheses using rigorous statistical methods.\n\nDesign analytical experiments and, where appropriate, evaluate results using structured experimentation or A/B testing methodologies.\n\nEnsure analytical approaches are appropriate for the intended operational application.\n\nModel Validation & Reliability\n\nApply rigorous validation techniques to ensure analytical models are accurate, reliable, explainable, and suitable for operational decision support.\n\nDocument model assumptions, limitations, intended uses, validation results, and operational considerations.\n\nEvaluate model performance against established requirements and success criteria.\n\nMonitor deployed models over time for changes in performance.\n\nIdentify model drift, data-quality issues, and changes in underlying operational conditions.\n\nSupport controlled model updates as data and operating conditions evolve.\n\nEnsure model outputs can be traced to approved source data.\n\nPromote reproducibility and repeatability throughout the analytical lifecycle.\n\nAzure Databricks Lakehouse & Cloud Analytics\n\nDesign and implement end-to-end analytics workflows within the Southern Nuclear Azure Databricks Lakehouse environment.\n\nBuild, maintain, and optimize analytical data pipelines.\n\nDevelop feature datasets supporting machine learning and advanced analytics.\n\nWork within enterprise medallion architecture standards and established data-management practices.\n\nLeverage Apache Spark and distributed computing technologies to process large and complex operational datasets.\n\nIntegrate data from approved enterprise data sources, data lakes, warehouses, and operational systems.\n\nDevelop scalable analytical solutions capable of supporting production workloads.\n\nCollaborate with data engineering and platform teams on data availability, architecture, integration, and performance.\n\nApply approved experiment tracking and model-lifecycle practices to support governance and repeatability.\n\nMLOps & Production Model Deployment\n\nSupport the development and implementation of repeatable machine learning training and deployment workflows.\n\nDeploy analytical and machine learning models into controlled production environments.\n\nParticipate in the development and improvement of MLOps practices.\n\nEstablish repeatable processes for model training, testing, validation, deployment, monitoring, and controlled updates.\n\nEnsure production models are maintainable, traceable, and appropriately documented.\n\nCoordinate with platform, engineering, and IT teams to ensure successful model integration.\n\nSupport production analytics used for operational and business decision-making.\n\nTime-Series & Nuclear Operational Analytics\n\nAnalyze industrial and operational time-series datasets.\n\nDevelop models capable of identifying abnormal equipment or system behavior.\n\nApply predictive analytics to support equipment reliability and predictive maintenance.\n\nEvaluate historical and real-time operational data to identify meaningful performance patterns.\n\nWork with nuclear engineers and operational subject-matter experts to understand plant telemetry and operational context.\n\nTranslate engineering and operational questions into measurable analytical problems.\n\nEnsure statistical conclusions appropriately reflect the characteristics and limitations of the underlying data.\n\nApplied AI & Advanced Analytics\n\nSupport approved AI-enabled analytical and search capabilities.\n\nEvaluate retrieval-based and other advanced AI techniques when appropriate for approved use cases.\n\nEnsure AI-assisted solutions are transparent, verifiable, traceable, and aligned with enterprise and regulatory expectations.\n\nEvaluate emerging data science and AI technologies for potential business and operational applications.\n\nPromote responsible and controlled implementation of advanced AI capabilities.\n\nEnsure AI-enabled solutions operate within established security, data-governance, and change-management requirements.\n\nData Governance, Security & Compliance\n\nDesign analytical solutions in accordance with enterprise data-governance standards.\n\nMaintain appropriate data access controls and security requirements.\n\nEnsure analytical outputs are traceable to approved data sources and inputs.\n\nFollow established data-handling and information-security standards.\n\nOperate within platform security controls designed to limit unauthorized data ingress, egress, and access.\n\nSupport auditability of data science models, analytical processes, and outputs.\n\nApply appropriate data privacy, ethical data-use, and compliance practices.\n\nEnsure analytical solutions meet Southern Company requirements for quality, accuracy, documentation, and auditability.\n\nWork in accordance with nuclear safety culture and formal change-management practices.\n\nData Visualization & Communication\n\nDevelop clear and effective visualizations that communicate complex analytical findings.\n\nPresent analytical insights to technical and non-technical stakeholders.\n\nUse tools such as Power BI, Tableau, Python visualization libraries, or other approved enterprise tools as appropriate.\n\nTranslate highly technical statistical and machine learning results into understandable business and operational information.\n\nClearly communicate model assumptions, methodologies, limitations, risks, and conclusions.\n\nDevelop presentations and supporting documentation for engineering, technology, analytics, and leadership stakeholders.\n\nCross-Functional Collaboration\n\nPartner closely with nuclear engineers, IT leaders, data engineers, platform teams, business analysts, domain experts, and analytics professionals.\n\nParticipate in technical design, architecture, and solution-development discussions.\n\nTranslate operational and engineering requirements into appropriate analytical approaches.\n\nCollaborate with IT and data engineering teams to integrate approved data sources.\n\nCoordinate with stakeholders throughout model development, validation, deployment, and operational support.\n\nBuild effective relationships across technical and operational organizations.\n\nServe as a senior data science resource on complex analytical initiatives.\n\nTechnical Leadership & Mentorship\n\nAs a senior-level Data Scientist, the successful candidate may also:\n\nProvide technical guidance and mentorship to junior data scientists and analytics professionals.\n\nReview analytical methodologies, model designs, and technical solutions.\n\nPromote data science standards and best practices.\n\nHelp improve organizational capabilities in machine learning, advanced analytics, MLOps, and AI.\n\nContribute to longer-term analytics and data science strategy.\n\nEvaluate new technologies, techniques, frameworks, and tools.\n\nEncourage innovation while maintaining appropriate governance and operational controls.\n\nRequired Qualifications\n\nApproximately 10–15 years of professional data science, advanced analytics, machine learning, or closely related experience.\n\nDemonstrated history of developing and implementing production-grade data science solutions.\n\nAdvanced proficiency in Python for data analysis, statistical modeling, and machine learning.\n\nStrong proficiency in SQL.\n\nExperience working with large-scale structured datasets.\n\nStrong understanding of statistical modeling and statistical validation.\n\nStrong experience with time-series analysis.\n\nExperience developing and deploying machine learning models in production cloud environments.\n\nExperience with big-data processing and distributed computing technologies.\n\nExperience with Apache Spark or similar large-scale processing frameworks.\n\nStrong understanding of machine learning algorithms and their practical applications.\n\nExperience integrating data from multiple enterprise sources.\n\nStrong analytical and critical-thinking capabilities.\n\nExcellent technical documentation skills.\n\nAbility to clearly explain analytical methodologies, assumptions, limitations, and results.\n\nStrong written and verbal communication skills.\n\nAbility to collaborate effectively with technical, operational, and business stakeholders.\n\nEducation\n\nMaster's degree preferred in one of the following or another related quantitative discipline:\n\nData Science\n\nComputer Science\n\nStatistics\n\nMathematics\n\nEngineering\n\nPhysics\n\nApplied Mathematics\n\nArtificial Intelligence/Machine Learning\n\nA Ph.D. in a relevant quantitative discipline may also be highly desirable depending on the candidate's professional experience.\n\nTechnical Skills\n\nStrong candidates should demonstrate experience with several of the following:\n\nPython\n\nSQL\n\nAzure Databricks\n\nApache Spark\n\nCloud-based analytics\n\nMachine learning\n\nStatistical modeling\n\nTime-series analysis\n\nPredictive modeling\n\nPredictive maintenance\n\nAnomaly detection\n\nFeature engineering\n\nDeep learning\n\nNeural networks\n\nEnsemble methods\n\nModel validation\n\nModel monitoring\n\nMLOps\n\nExperiment tracking\n\nData pipelines\n\nData lakes\n\nData warehouses\n\nMedallion architecture\n\nData integration\n\nData visualization\n\nPower BI\n\nTableau\n\nPython visualization libraries\n\nApplied AI\n\nRetrieval-based AI techniques\n\nEnterprise data governance\n\nAccess control\n\nModel lifecycle management\n\nPreferred Qualifications\n\nExperience supporting nuclear energy, utilities, power generation, energy, industrial operations, or another highly regulated industry.\n\nExperience with nuclear or industrial plant telemetry.\n\nExperience analyzing industrial or operational time-series data.\n\nAzure Databricks Lakehouse experience.\n\nExperience implementing scalable Spark-based analytics.\n\nExperience with enterprise medallion architecture.\n\nExperience developing predictive-maintenance or equipment-reliability models.\n\nExperience with anomaly-detection applications.\n\nMLOps experience, including repeatable model training and deployment workflows.\n\nExperience with enterprise data-governance frameworks.\n\nUnderstanding of role-based access controls and secure data environments.\n\nExperience working under formal change-management processes.\n\nExperience developing analytical solutions subject to audit or regulatory review.\n\nExperience implementing governed AI or advanced analytics capabilities.\n\nDocumentation & Quality Standards\n\nDocumentation is a critical component of this position. The Data Scientist will be expected to produce comprehensive technical documentation covering:\n\nAnalytical objectives and intended use\n\nSource data and approved inputs\n\nData preparation and transformation\n\nFeature engineering\n\nModel methodology\n\nAssumptions\n\nStatistical validation\n\nModel-performance results\n\nLimitations and risks\n\nDeployment methodology\n\nOperational considerations\n\nMonitoring requirements\n\nModel changes and updates\n\nAll work must support appropriate quality, accuracy, repeatability, traceability, maintainability, and auditability.\n\nKnowledge & Competencies\n\nThe successful candidate should demonstrate:\n\nExceptional analytical rigor and attention to detail.\n\nAdvanced problem-solving and critical-thinking abilities.\n\nAbility to work with complex and highly technical datasets.\n\nStrong understanding of statistical and machine learning principles.\n\nAbility to balance innovation with security, governance, and operational controls.\n\nStrong technical leadership capabilities.\n\nAbility to communicate effectively with engineers, technology professionals, analysts, and senior stakeholders.\n\nStrong organizational and project-management skills.\n\nAbility to independently manage complex analytical assignments.\n\nAbility to work effectively in a multidisciplinary environment.\n\nCommitment to ethical and responsible use of data and artificial intelligence.\n\nBehavioral Attributes\n\nThe successful candidate is expected to demonstrate Southern Company's core values and professional expectations, including:\n\nSafety First\n\nAct with Integrity\n\nIntentional Inclusion\n\nSuperior Performance\n\nCandidates should also demonstrate professionalism, accountability, collaboration, technical curiosity, attention to detail, and the ability to operate successfully in an environment with formal controls and regulatory oversight.\n\nNuclear & Regulatory Environment\n\nBecause this position supports Southern Nuclear operations, the selected candidate must be comfortable working in a highly regulated nuclear environment where data accuracy, cybersecurity, documentation, quality assurance, traceability, and formal change control are critical.\n\nThe position may require compliance with applicable nuclear regulatory and company requirements, including background screening, testing, training, security requirements, and other qualification processes, as required by policy.\n\nWork Location\n\n260 Southfield Parkway\nForest Park, GA 30297\n\nOn-site and work-location requirements will be aligned with Southern Company's business and operational needs.\n\nCompensation\n\n$50.00–$56.00 per hour, based on experience and qualifications.\n\nFinal compensation will consider the candidate's overall data science experience, Azure Databricks and cloud expertise, machine learning background, production deployment experience, nuclear/utility industry experience, and other relevant qualifications.\n\nWhy Join BLOC Resources?\n\nThis opportunity provides an experienced Data Scientist the ability to:\n\nSupport advanced analytics within the nuclear power industry.\n\nWork with complex nuclear operational and equipment data.\n\nDevelop production-grade machine learning solutions.\n\nWork within an enterprise Azure Databricks Lakehouse environment.\n\nContribute to predictive maintenance, equipment reliability, anomaly detection, and operational-performance initiatives.\n\nCollaborate directly with nuclear engineers, technology professionals, and analytics teams.\n\nGain exposure to governed AI and advanced analytics in a highly regulated environment.\n\nContribute to the modernization of data-driven nuclear operations.\n\nReceive ongoing recruiting and workforce support from BLOC Resources.\n\nIdeal Candidate Profile\n\nThe strongest candidate will be a senior Data Scientist with approximately 10–15 years of experience who combines advanced machine learning and statistical expertise with hands-on production implementation capabilities.\n\nThe candidate should be highly proficient in Python, SQL, Spark, cloud analytics, machine learning, time-series modeling, model validation, and production deployment.\n\nExperience with Azure Databricks, MLOps, industrial time-series data, predictive maintenance, equipment reliability, nuclear energy, utilities, or another highly regulated environment would make a candidate particularly competitive.\n\nMost importantly, this individual must understand that success in a nuclear environment requires more than building a high-performing model—the solution must also be explainable, controlled, documented, secure, repeatable, traceable, and auditable.\n\nEqual Employment Opportunity\n\nBLOC Resources is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment consistent with applicable federal, state, and local requirements.","datePosted":"2026-09-14T09:56:40.780Z","dateModified":"2026-09-14T09:56:40.780Z","hiringOrganization":{"@type":"Organization","name":"Bloc Resources","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Forest Park","addressRegion":"GA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"052d1a1beb50138983e9313e"},"url":"https://jobsearcher.com/jobs/052d1a1beb50138983e9313e"}}