{"schemaVersion":"jobsearcher.job.v1","id":"03f233f9e778daad2fd839af","url":"https://jobsearcher.com/jobs/03f233f9e778daad2fd839af","canonicalUrl":"https://jobsearcher.com/jobs/03f233f9e778daad2fd839af","title":"Data Scientist","description":"Job DescriptionJob Title: Senior Data Scientist – ML & Operational AnalyticsDuration: Long-termWork Schedule: Hybrid (Contract Full Time)Location: 701 9th Street. Northwest Washington, DC 200608Role OverviewThe Senior Data Scientist – ML & Operational Analytics will sit on the business-facing side of data science, partnering directly with operational and infrastructure stakeholders to define problems, build machine learning solutions, and deploy models into production.This role is not a backend data engineering or IT support position. It is a full‑lifecycle data science role focused on solving real business problems through predictive modeling, analytics, and AI.You will support multiple initiatives across Safety and Infrastructure Analytics, with a heavy emphasis on asset health, reliability, efficiency, and operational performance. Approximately 60% of the role is new model development, with the remaining 40% enhancing and maintaining existing models.Key ResponsibilitiesMachine Learning & Analytics Design, develop, and deploy machine learning models including regression, classification, and time‑series models for operational use cases.Apply advanced statistical and ML techniques to large‑scale datasets (terabytes to petabytes), including:Smart‑meter dataSmart‑grid and IoT dataStructured (relational databases)Unstructured data (text, documents, and limited multimedia)Perform feature engineering, data validation, and quality assessment to ensure model reliability and interpretability.Enhance existing models and pipelines while leading the development of net‑new solutions.Business Partnership & Problem Solving Work directly with business stakeholders to:Identify operational problemsTranslate business needs into analytical frameworksDefine success metrics and model outcomesClearly communicate analytical findings, model results, and recommendations to non‑technical audiences.Validate insights with the business and iterate based on feedback.Own solutions end‑to‑end: problem → data → model → deployment → business adoption.Data Science Lifecycle & Collaboration Collect, cleanse, standardize, and analyze data from multiple internal and external sources.Collaborate closely with:Information architectsData engineersProject and program managersOther data scientists and analystsEnsure smooth handoff and adoption of deployed solutions.Document methodologies, assumptions, and results to support governance and reuse.Act as a subject matter expert in machine learning, AI, feature engineering, data mining, and statistical modeling.Required Qualifications MS degree in Computer Science, Statistics, Mathematics, Engineering, Physics, or a related quantitative field (or 15+ years of equivalent professional data science experience)5+ years of hands‑on experience as a data scientist working on operational analytics or applied ML problems.Proven experience building and deploying ML models—not just training or research models.Strong proficiency in:Python (primary)RSQLCommon ML libraries (e.g., scikit‑learn, statsmodels, etc.)Strong foundation in:Probability and statistical inferenceRegression techniquesExperimental design and validationDemonstrated experience working closely with business stakeholders to deliver production solutions.Preferred Qualifications PhD in Computer Science, Statistics, Mathematics, Engineering, Physics, or related field.Experience within an Electric Utility, Energy, Infrastructure, or Industrial environment.Hands‑on experience with Azure Machine Learning for model development and deployment.Knowledge of optimization techniques, including:Linear programmingMixed‑integer optimizationExposure to:Computer visionGenerative AI use casesAzure certifications are a plus.","company":"Aroha Technologies","rawCompany":"aroha technologies","city":"Washington","state":"DC","isRemote":false,"isActive":false,"createdAt":"2026-04-25T12:32:47.748Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2031.00","title":"Operations Research Analysts","slug":"operations-research-analysts"}],"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":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Scientist","description":"Job DescriptionJob Title: Senior Data Scientist – ML & Operational AnalyticsDuration: Long-termWork Schedule: Hybrid (Contract Full Time)Location: 701 9th Street. Northwest Washington, DC 200608Role OverviewThe Senior Data Scientist – ML & Operational Analytics will sit on the business-facing side of data science, partnering directly with operational and infrastructure stakeholders to define problems, build machine learning solutions, and deploy models into production.This role is not a backend data engineering or IT support position. It is a full‑lifecycle data science role focused on solving real business problems through predictive modeling, analytics, and AI.You will support multiple initiatives across Safety and Infrastructure Analytics, with a heavy emphasis on asset health, reliability, efficiency, and operational performance. Approximately 60% of the role is new model development, with the remaining 40% enhancing and maintaining existing models.Key ResponsibilitiesMachine Learning & Analytics Design, develop, and deploy machine learning models including regression, classification, and time‑series models for operational use cases.Apply advanced statistical and ML techniques to large‑scale datasets (terabytes to petabytes), including:Smart‑meter dataSmart‑grid and IoT dataStructured (relational databases)Unstructured data (text, documents, and limited multimedia)Perform feature engineering, data validation, and quality assessment to ensure model reliability and interpretability.Enhance existing models and pipelines while leading the development of net‑new solutions.Business Partnership & Problem Solving Work directly with business stakeholders to:Identify operational problemsTranslate business needs into analytical frameworksDefine success metrics and model outcomesClearly communicate analytical findings, model results, and recommendations to non‑technical audiences.Validate insights with the business and iterate based on feedback.Own solutions end‑to‑end: problem → data → model → deployment → business adoption.Data Science Lifecycle & Collaboration Collect, cleanse, standardize, and analyze data from multiple internal and external sources.Collaborate closely with:Information architectsData engineersProject and program managersOther data scientists and analystsEnsure smooth handoff and adoption of deployed solutions.Document methodologies, assumptions, and results to support governance and reuse.Act as a subject matter expert in machine learning, AI, feature engineering, data mining, and statistical modeling.Required Qualifications MS degree in Computer Science, Statistics, Mathematics, Engineering, Physics, or a related quantitative field (or 15+ years of equivalent professional data science experience)5+ years of hands‑on experience as a data scientist working on operational analytics or applied ML problems.Proven experience building and deploying ML models—not just training or research models.Strong proficiency in:Python (primary)RSQLCommon ML libraries (e.g., scikit‑learn, statsmodels, etc.)Strong foundation in:Probability and statistical inferenceRegression techniquesExperimental design and validationDemonstrated experience working closely with business stakeholders to deliver production solutions.Preferred Qualifications PhD in Computer Science, Statistics, Mathematics, Engineering, Physics, or related field.Experience within an Electric Utility, Energy, Infrastructure, or Industrial environment.Hands‑on experience with Azure Machine Learning for model development and deployment.Knowledge of optimization techniques, including:Linear programmingMixed‑integer optimizationExposure to:Computer visionGenerative AI use casesAzure certifications are a plus.","datePosted":"2026-04-25T12:32:47.748Z","dateModified":"2026-04-25T12:32:47.748Z","hiringOrganization":{"@type":"Organization","name":"Aroha Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Washington","addressRegion":"DC","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"03f233f9e778daad2fd839af"},"url":"https://jobsearcher.com/jobs/03f233f9e778daad2fd839af"}}