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.