Data Scientist / Mid-level / Contract / Chicago / Hybrid

We are seeking a Data Scientist II to support clinical data research and operations for a leading global biopharmaceutical organization. This is a contract role based in Lake County, IL, with a hybrid schedule (on-site Tuesday–Thursday) and an ASAP start, currently slated through September 30 with strong potential for extension. In this role, you’ll work hands-on with Python, machine learning, statistical modeling, and AWS cloud technologies, helping design and scale data workflows that directly support R&D and scientific teams. This role sits at the intersection of data science, research, and real-world impact. You’ll work side-by-side with R&D scientists, influencing how clinical and research data is analyzed, modeled, and scaled for future growth. If you enjoy building and improving ML-driven datasets, collaborating across disciplines, and seeing your work directly support scientific innovation, this role delivers. The team is open to diverse backgrounds — including candidates from biotech or pharma who may not have held a formal “Data Scientist” title but bring strong statistical thinking and Python expertise. It’s a great opportunity to deepen your applied ML experience in a collaborative, research-focused environment while maintaining work-life balance with a predictable hybrid schedule. Contract Duration ASAP – September 30 (with possibility of extension) Required Skills & Experience • 3–5 years of hands-on experience in data science, analytics, or predictive modeling • Strong proficiency in Python for data analysis and modeling • Experience with statistical analysis and machine learning techniques • Ability to analyze and work with large datasets • Experience collaborating with cross-functional teams, including scientists or R&D partners • Bachelor’s degree with relevant professional experience Desired Skills & Experience • Background in pharma, biotech, or medical research • Experience scaling or improving data workflows • Familiarity with AWS cloud environments • Exposure to ML operations concepts • Working knowledge of SQL, relational or NoSQL databases, or data engineering concepts (ETL, data warehousing) • Familiarity with data visualization tools such as Tableau, Plotly, or Dash What You Will Be Doing Tech Breakdown • 50% Python, statistical analysis & machine learning • 25% Cloud & ML workflow support (AWS) • 15% Data visualization & analytics • 10% Data infrastructure & datasets support Daily Responsibilities • 70% Hands-On data analysis, modeling, and workflow development • 10% Strategic planning and problem solving • 20% Cross-team collaboration with R&D, scientists, and analytics partners Posted By: Ally Mitchell