Data Scientist/Mid-level/Chicago/Hybrid
Listed on 2026-02-16
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IT/Tech
Data Scientist, Data Analyst, Machine Learning/ ML Engineer, Data Engineer
Overview
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
- 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 No
SQL databases, or data engineering concepts (ETL, data warehousing) - Familiarity with data visualization tools such as Tableau, Plotly, or Dash
- 50% Python, statistical analysis & machine learning
- 25% Cloud & ML workflow support (AWS)
- 15% Data visualization & analytics
- 10% Data infrastructure & datasets support
- 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
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