Data Scientist; Azure​/Databricks​/Python

Overview

Title: Data Scientist (Azure/Databricks/Python)

Location: Deerfield, IL (4 days onsite per week)

Duration & Type: Initial 6-month Contract with chance for extension or conversion

Compensation: Competitive hourly W2 rate ($), Access to Healthcare & Dental Insurance Plan of Choice (details available upon request)

Summary

Chamberlain Advisors is partnering with a large, enterprise pharmaceutical retail organization to identify a highly skilled Data Scientist to design, develop, and deploy scalable machine learning solutions within a modern Azure cloud environment. This role sits within a high-impact analytics team and requires strong ownership across the full data lifecycle — from ingestion and modeling through production deployment and monitoring.

The ideal candidate brings a strong statistical foundation, deep hands-on experience with Azure Databricks and Python, and demonstrated success building and supporting production-grade machine learning pipelines s individual must be comfortable operating in complex, ambiguous problem spaces and capable of translating advanced analytics into measurable business outcomes. Click Apply Now to join the Chamberlain experience.

• Self-directed and comfortable navigating ambiguity.
• Strong ownership mindset across full model lifecycle.
• Ability to balance speed with engineering rigor.
• Continuous improvement mindset — willing to refactor and modernize legacy systems.
• Strong written and verbal communication skills.

• Apply statistical analysis and machine learning techniques to solve complex, high-dimensional business problems.
• Perform feature engineering and select appropriate modeling strategies aligned to business objectives.
• Develop, train, validate, and evaluate predictive models using appropriate performance metrics (AUC, precision/recall, RMSE, etc.).
• Apply statistical analysis, feature engineering, and machine learning techniques to solve complex, high-dimensional business problems and develop predictive and time-series models (ARIMA, Prophet, ML-based approaches), leveraging proper evaluation metrics (AUC, precision/recall, RMSE), hyperparameter tuning, cross-validation, and model explainability methods (SHAP, feature importance).
• Design and implement scalable analytics and modeling workflows in Azure Databricks using Spark (Data Frames, Spark SQL), optimizing compute performance for large-scale distributed datasets.
• Build, maintain, and modernize scalable ETL/ELT pipelines in Databricks, including incremental processing with Delta Lake, high-performance SQL for validation and reconciliation, and robust data quality monitoring and anomaly detection across millions to billions of records.
• Deploy, monitor, and support machine learning models in production environments, collaborating with engineering teams to operationalize solutions and ensure governance, reliability, and documentation of models and dependencies.
• Generate and test hypotheses through structured experimentation and product analysis, provide BI and dashboard support as needed, and partner with cross-functional stakeholders to translate business requirements into scalable analytics solutions.
• Demonstrate full lifecycle ownership from data ingestion through modeling, deployment, monitoring, and continuous improvement, clearly communicating insights and trade-offs to both technical and non-technical audiences.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or related quantitative field.
• 5–7 years of relevant data science experience.
• Strong proficiency in Python for analytics and production code.
• Solid foundation in:
  Statistics and probability;
  Feature engineering;
  Model evaluation techniques (AUC, precision/recall, RMSE, etc.).
• Hands-on experience with Azure for data science workloads.
• Strong familiarity with Azure Databricks.
• Experience developing:
  Regression models (linear and regularized);
  Tree-based models (Random Forest, XGBoost, Light
  
  GBM);
  Time-series forecasting models (ARIMA, Prophet, ML-based approaches).
• Strong SQL expertise for analytical and validation queries.
• Experience working with…