Data Scientist

Our Client, a Retail Pharmacy company, is looking for a Data Scientist 3 for their Deerfield, IL location.

• The main function of the data scientist is to produce innovative solutions driven by exploratory data analysis from complex and high-dimensional datasets.
• Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.
• Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data. Generate and test hypotheses and analyze and interpret the results of product experiments.
• Work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large-scale implementation. Provide Business Intelligence (BI) and data visualization support, which includes, but limited to support for the online customer service dashboards and other ad-hoc requests requiring data analysis and visual support.
• Data Science & Modeling
• Apply statistical and machine learning techniques to solve complex business problems.
• Perform feature engineering and select appropriate modeling approaches based on data and business context.
• Develop, train, validate, and evaluate machine learning models using appropriate metrics (e.g., AUC, precision/recall, RMSE).
• Build and maintain time-series forecasting models using approaches such as ARIMA, Prophet, or ML-based methods.
• Perform hyperparameter tuning, cross-validation, and model performance optimization.
• Provide model interpretability using techniques such as SHAP and feature importance.

• Develop data science solutions using Azure Databricks as the primary analytics platform.
• Leverage Spark (Data Frames and Spark SQL) to process large-scale datasets efficiently.
• Implement scalable workflows and jobs in Databricks to support batch modeling and inference.

• Design, build, and maintain robust ETL/ELT pipelines in Databricks.
• Write performant SQL for analytical queries, data validation, and reconciliation.
• Perform data quality checks, anomaly detection, and validation on large datasets.
• Handle and optimize datasets ranging from millions to billions of records.

• Support machine learning models in production environments, including monitoring and troubleshooting.
• Collaborate with engineering and platform teams to deploy and operationalize models.
• Refactor and modernize legacy pipelines and models to improve performance, scalability, and maintainability.
• Document models, pipelines, assumptions, and known limitations.

• Partner with business stakeholders, product managers, and engineers to translate business needs into data science solutions.
• Clearly communicate findings, model behavior, and trade-offs to both technical and non-technical audiences.
• Demonstrate strong ownership across the full lifecycle: data ingestion → modeling → deployment → monitoring.

• Experienced in either programming languages such as Python and/or R, big data tools such as Hadoop, or data visualization tools such as Tableau.
• The ability to communicate effectively in writing, including conveying complex information and promoting in-depth engagement on course topics. Experience working with large datasets.
• Master of Science degree in computer science or in a relevant field. 5-7 years of relevant experience required.
• Data Science & Modeling Fundamentals
  • Solid understanding of:
    Statistics and probability
  • Feature engineering techniques
  • Model evaluation metrics (e.g., AUC, precision/recall, RMSE)
  • Strong analytical thinking and problem-solving skills
• Azure & Databricks Experience
  • Hands-on experience using Azure for data science workloads
  • Strong familiarity with Azure Databricks for:
    Data processing, Model development, Production pipelines
• Machine Learning & Advanced Modeling
  • Ability to design, train, and validate machine learning models, including:
    Regression models (linear, regularized), Tree-based models (Random Forest, XGBoost, Light
    
    GBM), Time-series models (ARIMA, Prophet, or ML-based forecasting approaches)
• Experience with Hyperparameter tuning, Cross-validation techniques, Model explainability (e.g., SHAP, feature importance)
• Data Engineering & Pipeline Development
  • Ability to build and maintain scalable ETL/ELT pipelines in Databricks
  • Experience with Incremental data processing using Delta Lake, Writing strong, performant SQL for analytical queries, Data validation, reconciliation, and quality checks, Proven ability to work with large-scale datasets (millions to billions of records), Experience implementing data quality monitoring and anomaly detection
• Self-directed and comfortable working in ambiguous problem spaces.
• Strong ownership mindset across the full lifecycle:
  Data…