Our Client, a Retail Pharmacy company, is looking for a Data Scientist 3 for their Deerfield, IL location. Responsibilities:
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 proto@type 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 proto@types 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.
Azure & Databricks Development
Develop data science solutions using Azure Databricks as the primary analytics platform.
Leverage Spark (DataFrames and Spark SQL) to process large-scale datasets efficiently.
Implement scalable workflows and jobs in Databricks to support batch modeling and inference.
Data Engineering & Pipelines
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.
Production & Operational Excellence
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.
Collaboration & Ownership
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.
Requirements:
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, LightGBM)
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 ingestion ? modeling ? deployment ? monitoring
Experience supporting and maintaining models in production environments
Willingness to improve, refactor, and modernize legacy pipelines and models
Strong proficiency in Python for data analysis, modeling, and production code.
Solid understanding of:
Statistics and probability
Feature engineering
Model evaluation metrics (AUC, precision/recall, RMSE, etc.)
Hands-on experience with Azure for data science workloads.
Strong familiarity with Azure Databricks.
Experience developing machine learning models including:
Regression (linear and regularized)
Tree-based models (Random Forest, XGBoost, LightGBM)
Time-series models (ARIMA, Prophet, or similar)
Strong SQL skills for analytics and data validation.
Experience working with large-scale datasets in distributed environments.
Experience with performance tuning.
Familiarity with MLOps practices (model versioning, retraining strategies, monitoring).
Experience working in regulated or enterprise environments.
Domain experience in retail, supply chain, or related industries.
Why Should You Apply?
Health Benefits
Referral Program
Excellent growth and advancement opportunities
As an equal opportunity employer, ICONMA provides an employment environment that supports and encourages the abilities of all persons without regard to race, color, religion, gender, sexual orientation, gender identity or express, ethnicity, national origin, age, disability status, political affiliation, genetics, marital status, protected veteran status, or any other characteristic protected by federal, state, or local laws.
