Data Science Engineer

Job Overview

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.

Key Responsibilities

• 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
• 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

Desired Traits

• 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

Required Skills And Experience

• 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