> Markdown version of [/jobs/ext/1802864-data-science-engineer](https://www.wearedevelopers.com/jobs/ext/1802864-data-science-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Science Engineer - **Company:** Spectrum Communications - **Location:** Boca Raton, FL, United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), DevOps, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Query Optimization, Azure DevOps Pipelines, Standard Sql, Azure Machine Learning, SQL Databases, Azure Data Factory, Pytorch, Large Language Models, Apache Spark, Pandas, Git Flow, Scikit Learn, Information Technology, Dask, Machine Learning Operations, Azure Synapse Analytics, Software Version Control, Data Pipelines, Docker, Key Vault, Databricks - **Published:** July 2, 2026 - **Apply:** https://www.spectruminc.com/careers/detail/?id=797393948652773150 ## About the Role We 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: * Ownership mindset with a reliability-first approach * Strong SQL/Python and a high attention to data quality * Scales systems thoughtfully (performance/cost aware, maintainable designs) * Collaborative communicator across engineering, RevOps, and analytics * Documents well and supports others through reviews/mentorship, * Preferred Master's degree in Data Science, Computer Science, Statistics, Engineering, or a closely related quantitative field. * 4+ years in data engineering, ML engineering, or data platform development. * Minimum 2 years deploying ML models into production workflows. * Experience building pipelines and warehouse systems at scale (>100GB datasets). * Demonstrated adaptability in fast-changing technical and business environments. * Python (Expert): pandas, polars, scikit-learn; PyTorch, transformers; production engineering (OOP, testing, typing) * SQL (Expert): advanced analytics, recursive CTEs, query tuning, Azure Synapse optimization * Azure Data & ML Stack: Data Factory (ETL/ELT), Azure ML (MLOps), Key Vault, Databricks/Spark, Docker deployment * Distributed & Large-Scale Compute: Spark, Ray, Dask; GPU acceleration with RAPIDS (plus) * Geospatial & Specialized Data: GeoPandas, Shapely, rasterio * AI Automation & LLMs: LangChain/Semantic Kernel, agentic workflows * DevOps & CI/CD: Azure DevOps pipelines, Gitflow, rebasing, clean version control ## Description As 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., * 4+ years in data-centric engineering * Proven experience deploying ML models via pipelines * Deep expertise in Python, SQL, and Azure infrastructure * Architectural ownership through data contracts and resilient modeling * Build scalable batch and near-real-time ingestion pipelines using Azure Data Factory, APIs, event streams, and external connectors. * Develop ML-ready datasets across CRM, marketing automation platforms, product telemetry, and geospatial data sources. * Design performant, well-modeled warehouse/lakehouse systems in Azure Synapse or Databricks. * Train and deploy predictive models (lead scoring, churn prediction, forecasting) through reproducible pipelines. * Build time-aware, leakage-resistant feature pipelines for production ML use cases. * Support full MLOps lifecycle using Azure Machine Learning, including experiment tracking, model registry, and deployment. * Implement automated validation, anomaly detection, reconciliation, and monitoring for pipelines and warehouse models. * Design and enforce data contracts to prevent upstream schema changes from breaking downstream ML workflows. * Own pipeline SLAs, alerting, incident response, and durable improvements through postmortems. * Optimize processing for very large datasets (>100GB) through partitioning, incremental loads, distributed compute, and query tuning. * Improve cost efficiency across compute/storage in Azure environments. * Maintain clean, testable, production-ready Python codebases using: * Object-oriented patterns * Type hinting * CI/CD workflows via Azure DevOps * Package models and pipelines using Docker for consistent deployment across dev/staging/prod. * Communicate architectural trade-offs and technical debt in business terms to Product, RevOps, and leadership. * Partner with Engineering on instrumentation and scalable data integration. * Mentor junior engineers through pairing, code reviews, and documentation best practices. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)