AffirmedRx, PBC - Data Scientist
Requirements
• 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 • Note: • 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.
Responsibilities
• Machine Learning and Predictive Modeling: • Build predictive and prescriptive ML models for pharmacy cost and risk (e.g., forecasting second-year member spend), including feature engineering, model selection, and explainability analysis (e.g., SHAP-based feature attribution) • Develop member-level risk and comorbidity scoring, mapping drug identifiers (NDC → ATC) to clinical conditions and severity weights, and validating outputs against edge cases • Applied AI and Natural Language Processing: • Use AI/NLP to analyze unstructured member and clinical text — sentiment analysis, topic modeling, and tokenization of open-ended survey and feedback data • Contribute to the organization’s broader AI direction: evaluating models, defining evaluation/answer-key datasets, and building drift and validation checks for AI outputs • Member Identity Resolution and Data Quality: • 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 • Clinical and Pharmacy Analytics: • 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 • Analytical Tooling and Delivery: • 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 • Validation, Documentation, and Collaboration: • 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
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