Data Scientist

• Use AI/NLP to analyze unstructured member and clinical text — sentiment analysis, topic modeling, and tokenization of open-ended survey and feedback data
• Apply AI tooling (LLMs / copilots and internal AI services) to automate clinical policy and documentation workflows, including prompt design, output validation, and controls against hallucination and format drift
• Contribute to the organization’s broader AI direction: evaluating models, defining evaluation/answer-key datasets, and building drift and validation checks for AI outputs

• Design and maintain probabilistic (fuzzy) matching logic to assign and reconcile unique member identifiers across carriers and source systems, including collision handling, cluster analysis, and audit/logging frameworks
• Monitor and improve match rates, investigate false positives and fragmentation, and document data lineage and safeguards against duplicates

• Produce clinical and pharmacy analytics such as medication adherence and persistence (drug-, class-, and NDC-level), aligned to compliance requirements (e.g., URAC / PQA measures)
• QA and validate reporting products (e.g., pharmacy trend dashboards, PMPM metrics), reconciling data-point discrepancies across source systems

• Build analytical tools and prototypes (e.g., formulary/tier decision tools and cost-comparison tools), including lightweight front ends (e.g., Streamlit) for sales, clinical, and pricing use
• Deliver validated datasets and tables into the data warehouse in partnership with data engineering, and support the transition of prototypes into production

• Own QA and validation for analytical outputs, including auditing of claims files and validation of model results before release
• Document models, logic, data sources, schedules, and troubleshooting steps to make work reproducible and auditable
• Collaborate across clinical, reporting, pricing, client-success, and engineering stakeholders to gather requirements and translate them into analytical specifications

What you need:

• Degree in a quantitative field (data science, statistics, computer science, applied math) or equivalent experience
• 2–3 years of experience using Python and SQL for data analysis, machine learning, NLP, data quality, and record-matching solutions. Data modeling experience in PBM and/or healthcare industry in general preferred
• Strong Python for data science and ML (e.g., pandas plus a modeling stack), and proficiency in SQL
• Demonstrated experience building and validating ML models, including feature engineering and model explainability
• Experience with NLP techniques (sentiment analysis, topic modeling) and with applying AI/LLM tooling to real workflows, including output validation
• Experience with entity resolution / probabilistic record matching and data-quality analysis
• Comfort working with a modern cloud data warehouse and data lake, and partnering with data engineering on production hand-off
• Healthcare, pharmacy benefit management (PBM), or claims-data experience
• Familiarity with pharmacy data concepts (NDC, GPI, ATC, formulary tiers, prior authorization, rebates)
• Experience with compliance-driven reporting (e.g., URAC / PQA measures)
• Experience building analytical front ends or dashboards (e.g., Streamlit, BI tools) for non-technical stakeholders
• Willingness and ability to travel (10%-20%)

What you get:

• To impact industry change in the pharmacy benefits management space, while delivering the highest quality patient outcomes
• To work in a culture where people thrive because when OUR team thrives, OUR business thrives
• Competitive compensation, including health, dental, vision and other benefits

AffirmedRx is committed to providing equal employment opportunities to all employees and applicants for employment.

Remote employees are expected to maintain a professional work environment free of distractions to ensure optimal performance and collaboration.