Data Science Engineer — ML Pipelines (Remote/Hybrid)

Predictive Sales AI, LLC; Chicago (IL); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Predictive Sales AI is seeking a Data Science Engineer to design and operate the data and ML foundations behind PSAI’s predictive products. You will build scalable pipelines and robust warehouse/lakehouse models across CRM, marketing, product events, and external datasets.

The role emphasizes production-grade ML, MLOps, and Azure infrastructure, with opportunities to mentor juniors and collaborate with RevOps, analytics, and engineering.

Qualifications

• 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

Responsibilities

• 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 ML, 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 OO patterns, typing, CI/CD, Docker packaging.
• Communicate architectural trade-offs and technical debt in business terms.
• Partner with Engineering on instrumentation and scalable data integration.
• Mentor junior engineers through pairing, code reviews, and documentation best practices.

Skills

Data engineering

ML pipelines

Python

SQL

Azure infrastructure

MLOps

Docker

Spark/Ray/Dask

Geospatial data

Education

Master's degree in Data Science / CS / Statistics / Engineering

Tools

Azure Data Factory

Azure Synapse

Databricks

Docker deployment

Azure ML

Spark

Job description

Predictive Sales AI is seeking a Data Science Engineer to design and operate the data and ML foundations behind PSAI’s predictive products. You will build scalable pipelines and robust warehouse/lakehouse models across CRM, marketing, product events, and external datasets.

The role emphasizes production-grade ML, MLOps, and Azure infrastructure, with opportunities to mentor juniors and collaborate with RevOps, analytics, and engineering.