BioVid
Healthcare Data Scientist & AI Solutions
About this role
BioVid is transforming pharmaceutical market research by replacing slow, traditional methodologies with AI-augmented data systems. In this role, you will personally analyze the data to uncover HCP prescribing behavior and behavioral insights. You will help bridge quantitative real-world data with qualitative market research, and play a key role in developing synthetic audiences (“Digital Twins”) that mirror healthcare provider prescribing behavior and patient treatment journeys, while enabling scalable forecasting, segmentation, and market intelligence solutions. Must-Have Qualifications Hands-on analysis of medical / pharmacy claims data (EHR preferred) Direct, hands-on experience analyzing the data itself — not only engineering it. You can extract and predict HCP prescribing behavior for specific drugs, for specific conditions, in specific treatment areas, and tag HCP qualitative / attitudinal segments to NPIs, associating those segments with real prescribing behavior. HIPAA requirements fluent (most of our data will be deidentified)
- Analyze medical and pharmacy claims to extract and predict HCP prescribing behavior for specific drugs, conditions, and treatment areas.
- Perform HCP / patient segmentation, demand forecasting, journey mapping, modeling
- Help build Synthetics, simulated segments and HCP equivalents.
- Machine Learning experience in modeling training and evaluating models (LLM fine tuning, training a nice to have)
- Tag qualitative / attitudinal HCP segments to NPIs and link them to observed prescribing behavior.
- Translate these analyses into actionable segmentation, targeting, and forecasting insights. Data Cleaning, Processing, Augmentation and Transformation
- Data cleaning, scarcity, gap analysis, joining and data augmentation, transformation to create partitions, data marts and analytics workspaces for analysis and modeling in AWS/Athena. Key Responsibilities Data Integration & Modeling
- Data cleaning, scarcity, gap analysis, joining and data augmentation, transformation to create partitions, data marts and analytics workspaces for analysis and modeling inAWS/Athena.
- Resolve identity matching, tokenization, and data harmonization challenges across disparate sources.
- Build scalable, tested, and version-controlled data models using dbt on AWS/Athena. Data Quality, Governance & Compliance
- Implement automated data quality checks using tools such as Great Expectations or equivalent. Analytics, AI & Machine Learning