{"schemaVersion":"jobsearcher.job.v1","id":"b5b8dce13e2ada8bbd5d374a","url":"https://jobsearcher.com/jobs/b5b8dce13e2ada8bbd5d374a","canonicalUrl":"https://jobsearcher.com/jobs/b5b8dce13e2ada8bbd5d374a","title":"Data Science Engineer","description":"Job Title: Data Science Engineer\r\nLocation: Boca Raton, FL-Remote/Hybrid\r\nAbout Us\r\nAt Predictive Sales AI (PSAI), we're redefining how technology and intelligence transform digital marketing. Our AI-powered software enables home services businesses to make smarter, faster decisions—fueling growth through automation, prediction, and precision.\r\nWe are seeking a Data Science Engineer with strong data engineering and MLOps expertise to build scalable, production-grade ML and data platforms that directly impact customer growth and retention.\r\nJob Overview\r\nAs a Data Science Engineer , you will design and operate the data + machine learning foundations behind PSAI's predictive products. You will build scalable pipelines and robust warehouse/lakehouse models across CRM, marketing, product events, and external datasets — ensuring reliability, accuracy, and business continuity at scale.\r\nKey Responsibilities\r\n4+ years in data-centric engineering\r\nProven experience deploying ML models via pipelines\r\nDeep expertise inPython, SQL, and Azure infrastructure\r\nArchitectural ownership throughdata contractsand resilient modeling\r\nBuild scalable batch and near-real-time ingestion pipelines usingAzure Data Factory, APIs, event streams, and external connectors.\r\nDevelop ML-ready datasets across CRM, marketing automation platforms, product telemetry, and geospatial data sources.\r\nDesign performant, well-modeled warehouse/lakehouse systems inAzure Synapseor Databricks.\r\nTrain and deploy predictive models (lead scoring, churn prediction, forecasting) through reproducible pipelines.\r\nBuild time-aware, leakage-resistant feature pipelines for production ML use cases.\r\nSupport full MLOps lifecycle usingAzure Machine Learning, including experiment tracking, model registry, and deployment.\r\nImplement automated validation, anomaly detection, reconciliation, and monitoring for pipelines and warehouse models.\r\nDesign and enforcedata contractsto prevent upstream schema changes from breaking downstream ML workflows.\r\nOwn pipeline SLAs, alerting, incident response, and durable improvements through postmortems.\r\nOptimize processing for very large datasets (>100GB) through partitioning, incremental loads, distributed compute, and query tuning.\r\nImprove cost efficiency across compute/storage in Azure environments.\r\nMaintain clean, testable, production-ready Python codebases using:\r\nObject-oriented patterns\r\nType hinting\r\nCI/CD workflows viaAzure DevOps\r\nPackage models and pipelines using Docker for consistent deployment across dev/staging/prod.\r\nCommunicate architectural trade-offs and technical debt in business terms to Product, RevOps, and leadership.\r\nPartner with Engineering on instrumentation and scalable data integration.\r\nMentor junior engineers through pairing, code reviews, and documentation best practices.\r\nDesired Traits\r\nWe are looking for an individual who is organized, proactive, and detail-oriented. In this role, you will work closely with teams across the company. Here's what we're looking for:\r\nOwnership mindset with a reliability-first approach\r\nStrong SQL/Python and a high attention to data quality\r\nScales systems thoughtfully (performance/cost aware, maintainable designs)\r\nCollaborative communicator across engineering, RevOps, and analytics\r\nDocuments well and supports others through reviews/mentorship\r\nRequired Skills and Experience\r\nPreferred Master's degree in Data Science, Computer Science, Statistics, Engineering, or a closely related quantitative field.\r\n4+ yearsin data engineering, ML engineering, or data platform development.\r\nMinimum2 years deploying ML models into production workflows.\r\nExperience building pipelines and warehouse systems at scale (>100GB datasets).\r\nDemonstrated adaptability in fast-changing technical and business environments.\r\nPython (Expert):pandas, polars, scikit-learn; PyTorch, transformers; production engineering (OOP, testing, typing)\r\nSQL (Expert):advanced analytics, recursive CTEs, query tuning, Azure Synapse optimization\r\nAzure Data & ML Stack:Data Factory (ETL/ELT), Azure ML (MLOps), Key Vault, Databricks/Spark, Docker deployment\r\nDistributed & Large-Scale Compute:Spark, Ray, Dask; GPU acceleration with RAPIDS (plus)\r\nGeospatial & Specialized Data:GeoPandas, Shapely, rasterio\r\nAI Automation & LLMs:LangChain/Semantic Kernel, agentic workflows\r\nDevOps & CI/CD:Azure DevOps pipelines, Gitflow, rebasing, clean version control\r\nWhy Join Us?\r\nInnovative Environment: Be part of a forward-thinking company that values creativity and encourages the exploration of new ideas.\r\nProfessional Growth: Access opportunities for continuous learning and career advancement within a supportive and dynamic team.\r\nComprehensive Benefits: Enjoy a competitive salary, performance-based bonuses, flexible work arrangements, and a robust benefits package.\r\nCollaborative Culture: Work in a team-oriented environment where collaboration and mutual respect drive our success.\r\nJ-18808-Ljbffr","company":"Spectrum","rawCompany":"spectrum","city":"Boca Raton","state":"FL","isRemote":false,"isActive":false,"createdAt":"2026-07-16T01:47:46.664Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Science Engineer","description":"Job Title: Data Science Engineer\r\nLocation: Boca Raton, FL-Remote/Hybrid\r\nAbout Us\r\nAt Predictive Sales AI (PSAI), we're redefining how technology and intelligence transform digital marketing. Our AI-powered software enables home services businesses to make smarter, faster decisions—fueling growth through automation, prediction, and precision.\r\nWe are seeking a Data Science Engineer with strong data engineering and MLOps expertise to build scalable, production-grade ML and data platforms that directly impact customer growth and retention.\r\nJob Overview\r\nAs a Data Science Engineer , you will design and operate the data + machine learning foundations behind PSAI's predictive products. You will build scalable pipelines and robust warehouse/lakehouse models across CRM, marketing, product events, and external datasets — ensuring reliability, accuracy, and business continuity at scale.\r\nKey Responsibilities\r\n4+ years in data-centric engineering\r\nProven experience deploying ML models via pipelines\r\nDeep expertise inPython, SQL, and Azure infrastructure\r\nArchitectural ownership throughdata contractsand resilient modeling\r\nBuild scalable batch and near-real-time ingestion pipelines usingAzure Data Factory, APIs, event streams, and external connectors.\r\nDevelop ML-ready datasets across CRM, marketing automation platforms, product telemetry, and geospatial data sources.\r\nDesign performant, well-modeled warehouse/lakehouse systems inAzure Synapseor Databricks.\r\nTrain and deploy predictive models (lead scoring, churn prediction, forecasting) through reproducible pipelines.\r\nBuild time-aware, leakage-resistant feature pipelines for production ML use cases.\r\nSupport full MLOps lifecycle usingAzure Machine Learning, including experiment tracking, model registry, and deployment.\r\nImplement automated validation, anomaly detection, reconciliation, and monitoring for pipelines and warehouse models.\r\nDesign and enforcedata contractsto prevent upstream schema changes from breaking downstream ML workflows.\r\nOwn pipeline SLAs, alerting, incident response, and durable improvements through postmortems.\r\nOptimize processing for very large datasets (>100GB) through partitioning, incremental loads, distributed compute, and query tuning.\r\nImprove cost efficiency across compute/storage in Azure environments.\r\nMaintain clean, testable, production-ready Python codebases using:\r\nObject-oriented patterns\r\nType hinting\r\nCI/CD workflows viaAzure DevOps\r\nPackage models and pipelines using Docker for consistent deployment across dev/staging/prod.\r\nCommunicate architectural trade-offs and technical debt in business terms to Product, RevOps, and leadership.\r\nPartner with Engineering on instrumentation and scalable data integration.\r\nMentor junior engineers through pairing, code reviews, and documentation best practices.\r\nDesired Traits\r\nWe are looking for an individual who is organized, proactive, and detail-oriented. In this role, you will work closely with teams across the company. Here's what we're looking for:\r\nOwnership mindset with a reliability-first approach\r\nStrong SQL/Python and a high attention to data quality\r\nScales systems thoughtfully (performance/cost aware, maintainable designs)\r\nCollaborative communicator across engineering, RevOps, and analytics\r\nDocuments well and supports others through reviews/mentorship\r\nRequired Skills and Experience\r\nPreferred Master's degree in Data Science, Computer Science, Statistics, Engineering, or a closely related quantitative field.\r\n4+ yearsin data engineering, ML engineering, or data platform development.\r\nMinimum2 years deploying ML models into production workflows.\r\nExperience building pipelines and warehouse systems at scale (>100GB datasets).\r\nDemonstrated adaptability in fast-changing technical and business environments.\r\nPython (Expert):pandas, polars, scikit-learn; PyTorch, transformers; production engineering (OOP, testing, typing)\r\nSQL (Expert):advanced analytics, recursive CTEs, query tuning, Azure Synapse optimization\r\nAzure Data & ML Stack:Data Factory (ETL/ELT), Azure ML (MLOps), Key Vault, Databricks/Spark, Docker deployment\r\nDistributed & Large-Scale Compute:Spark, Ray, Dask; GPU acceleration with RAPIDS (plus)\r\nGeospatial & Specialized Data:GeoPandas, Shapely, rasterio\r\nAI Automation & LLMs:LangChain/Semantic Kernel, agentic workflows\r\nDevOps & CI/CD:Azure DevOps pipelines, Gitflow, rebasing, clean version control\r\nWhy Join Us?\r\nInnovative Environment: Be part of a forward-thinking company that values creativity and encourages the exploration of new ideas.\r\nProfessional Growth: Access opportunities for continuous learning and career advancement within a supportive and dynamic team.\r\nComprehensive Benefits: Enjoy a competitive salary, performance-based bonuses, flexible work arrangements, and a robust benefits package.\r\nCollaborative Culture: Work in a team-oriented environment where collaboration and mutual respect drive our success.\r\nJ-18808-Ljbffr","datePosted":"2026-07-16T01:47:46.664Z","dateModified":"2026-07-16T01:47:46.664Z","hiringOrganization":{"@type":"Organization","name":"Spectrum","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Boca Raton","addressRegion":"FL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b5b8dce13e2ada8bbd5d374a"},"url":"https://jobsearcher.com/jobs/b5b8dce13e2ada8bbd5d374a"}}