Data Scientist
Hybrid – Indianapolis, IN
About the Role
We are seeking a Data Scientist with 3–5 years of experience working specifically within the pharma industry to join a pharma-focused data team. This role combines applied statistical/ML modeling with strong data engineering fluency, working against large-scale data housed in Databricks and AWS. You will partner with business groups to frame problems, build models and analyses that answer them, and communicate results in terms that drive pharma business decisions.
Key Responsibilities
Develop statistical models, machine learning models, and advanced analyses using large-scale datasets in Databricks
Access, prepare, and engineer features from data processed through Apache Spark and AWS data services
Partner with business groups to understand pharma-specific problems and translate them into data science approaches
Build and validate ETL/data pipelines as needed to support modeling and experimentation workflows
Communicate modeling results, insights, and recommendations clearly to both technical and non-technical business stakeholders
Apply pharma domain knowledge to ensure models and analyses are relevant and interpretable in a business context
Collaborate with data engineers and analysts to productionize models and integrate outputs into reporting/decision workflows
Monitor model performance over time and iterate as needed
Requirements
Required Qualifications
3–5 years of data science / applied statistics / machine learning experience specifically within the pharma industry
Hands-on experience with Databricks for data science/ML workflows
Working knowledge of AWS data services
Strong experience with Big Data processing using Apache Spark, PySpark.
Experience building ETL pipelines to support data science workflows
Strong Python and SQL skills; experience with ML libraries
Demonstrated ability to understand pharma business needs and speak to pharma business groups
Strong communication skills with demonstrated ability to present technical findings to business stakeholders
Bachelor's or master's degree in data science, Statistics, Computer Science, or a related quantitative field
Preferred Qualifications
Experience with Delta Lake, Snowflake, or similar modern data platforms
Familiarity with MLOps practices and model deployment/monitoring
Prior experience supporting pharma commercial, clinical, or R&D data science functions