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# Data Science Engineer

- **Company:** Predictive Sales AI
- **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, Azure Machine Learning, SQL Databases, Azure Data Factory, Pytorch, Large Language Models, Apache Spark, Technical Debt, Agentic-AI, Pandas, Git Flow, Scikit Learn, Information Technology, Dask, Machine Learning Operations, Semantic Kernel, Azure Synapse Analytics, Software Version Control, Data Pipelines, Docker, Key Vault, Databricks
- **Published:** October 7, 2026
- **Apply:** https://www.predictivesalesai.com/careers/detail/?id=797392798551460862

## 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.