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
- Conduct advanced exploratory data analysis (EDA) and feature engineering using SQL, Python, pandas, and numpy.
- Analyze claims data to model and predict HCP prescribing behavior by drug, condition, and treatment area, and associate attitudinal segments with NPIs.
- Build AI-powered synthetic provider and patient personas grounded in real-world claims distributions.
- Develop methods to scale qualitative research insights into national quantitative projections.
- Machine Learning experience in modeling training and evaluating models. (LLM fine tuning and training a nice to have
- Perform (or help perform) and automate pharmaceutical market research including:
- HCP segmentation
- Demand forecasting
- Patient journey mapping
- Audience simulation
API & Product Delivery
- Build and maintain performant backend APIs using FastAPI, deployed on AWS (e.g., ECS/Fargate, App Runner, or Lambda).
- Deliver data products, personas, and insights to internal tools and client-facing applications.
Skills & Experience
- 5+ years in Analytics Engineering, Data Engineering, Data Science, or similar roles.
- Proven ownership of end-to-end data pipelines and scalable analytics systems on AWS.
- Hands-on experience with AWS data and analytics tooling, including Athena, S3, Glue, and SageMaker (AWS is the primary environment).
- Strong experience with healthcare claims and EHR data (Rx, Dx, medical, pharmacy) and major data vendors such as IQVIA, Komodo, Symphony, or Definitive Healthcare.
- Demonstrated hands-on analysis of medical/pharmacy claims to extract and predict HCP prescribing behavior and tag attitudinal segments to NPIs.
- Expert SQL and strong Python skills, including pandas and large-scale data workflows.
- Understanding of pharma market research, including patient journeys, HCP segmentation, and demand modeling.
- Experience integrating survey or primary research data with large quantitative datasets.
- Comfortable using AI coding assistants and agentic development tools to accelerate delivery.