Predictive Sales AI a Spectrum Communications & Consulting LLC Brand · 1 day ago
Data Science Engineer
Chicago, IL
Full-time
Hybrid
Mid, Senior Level
4+ years expPredictive Sales AI is redefining how technology and intelligence transform digital marketing. They are seeking a Data 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.
ConsultingInternetInternet of ThingsTelecommunications
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 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
Qualification
Required
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
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
Benefits
Competitive salary
Performance-based bonuses
Flexible work arrangements
Robust benefits package
Company
Predictive Sales AI a Spectrum Communications & Consulting LLC Brand
With proven success across many industries over 30 years, we have accumulated the blueprint to scaling your home services business.
51-200 employees
Funding
Current Stage
Growth StageLeadership Team
Ty King
President & CEO
Company data provided by crunchbase