Data Science Engineer

Predictive Sales AI, LLC; Chicago (IL); Ibrido; USD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

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.

Competenze

• 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

Mansioni

• 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.

Conoscenze

Data engineering

ML pipelines

Python

SQL

Azure infrastructure

MLOps

Docker

Spark/Ray/Dask

Geospatial data

Formazione

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

Strumenti

Azure Data Factory

Azure Synapse

Databricks

Docker deployment

Azure ML

Spark

Descrizione del lavoro

Job Title:Data Science Engineer
Location: Chicago, IL-Remote/Hybrid

About Us:

AtPredictive 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.

We are seeking aData 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.

Job Overview:

As aData 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.

This role requires:

Key Responsibilities:

• 4+ years in data-centric engineering
• Proven experience deploying ML models via pipelines
• Deep expertise inPython, SQL, and Azure infrastructure
• Architectural ownership throughdata contractsand resilient modeling

• Build scalable batch and near-real-time ingestion pipelines usingAzure 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 inAzure Synapseor 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 usingAzure Machine Learning, including experiment tracking, model registry, and deployment.
• Implement automated validation, anomaly detection, reconciliation, and monitoring for pipelines and warehouse models.
• Design and enforcedata contractsto 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 viaAzure 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:

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

Required Skills and Experience:

• Preferred Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a closely related quantitative field.
• 4+ yearsin data engineering, ML engineering, or data platform development.
• Minimum2 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

Why Join Us?

• Innovative Environment:Be part of a forward-thinking company that values creativity and encourages the exploration of new ideas.
• Professional Growth:Access opportunities for continuous learning and career advancement within a supportive and dynamic team.
• Comprehensive Benefits:Enjoy a competitive salary, performance-based bonuses, flexible work arrangements, and a robust benefits package.
• Collaborative Culture:Work in a team-oriented environment where collaboration and mutual respect drive our success.