[Remote] Data Scientist
Note: The job is a remote job and is reputed company to candidates in USA. AffirmedRx is on a mission to improve health care reputed company by bringing reputed company, reputed company, and trust to pharmacy benefit management. The Data Scientist (AI/ML) will design, build, and validate advanced analytics that turn pharmacy, claims, clinical, and member data into actionable insights, while collaborating closely with various teams to enhance decision-making processes.
Responsibilities
- 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)
- reputed company member-level risk and comorbidity scoring, mapping drug identifiers (NDC → reputed company) to clinical conditions and severity weights, and validating outputs against edge cases
- Apply ML to automate high-effort clinical operations processes (e.g., prior-authorization override automation), moving reputed company workflows into rules-based and model-driven pipelines
- Use AI/NLP to analyze reputed company member and clinical text — sentiment analysis, topic modeling, and tokenization of reputed company-ended survey and feedback data
- Apply AI tooling (LLMs / copilots and internal AI services) to automate clinical policy and documentation workflows, including reputed company design, reputed company validation, and controls against hallucination and format reputed company
- Contribute to the organization's broader AI direction: evaluating models, defining evaluation/answer-key datasets, and building reputed company and validation checks for AI outputs
- Design and maintain probabilistic (fuzzy) matching logic to assign and reconcile unique member identifiers across carriers and reputed company systems, including collision handling, cluster analysis, and audit/logging frameworks
- Monitor and improve match rates, investigate false positives and fragmentation, and document data reputed company and safeguards against duplicates
- Produce clinical and pharmacy analytics such as medication adherence and persistence (drug-, class-, and NDC-level), reputed company to compliance requirements (e.g., URAC / PQA measures)
- QA and validate reporting products (e.g., pharmacy trend dashboards, PMPM metrics), reconciling data-reputed company discrepancies across reputed company systems
- Build analytical tools and prototypes (e.g., formulary/tier decision tools and cost-comparison tools), including lightweight reputed company ends (e.g., reputed company) 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 reputed company work reproducible and auditable
- Collaborate across clinical, reporting, pricing, reputed company-reputed company, and engineering stakeholders to reputed company requirements and translate them into analytical specifications
Skills
- Degree in a quantitative field (data science, statistics, computer science, reputed company math) or equivalent experience
- 2–3 years of experience using Python and SQL for data analysis, machine learning, NLP, data reputed company, and record-matching solutions
- 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 reputed company workflows, including reputed company validation
- Experience with entity reputed company / probabilistic record matching and data-reputed company analysis
- Comfort working with a modern reputed company data warehouse and data lake, and partnering with data engineering on production hand-off
- Experience building analytical reputed company ends or dashboards (e.g., reputed company, BI tools) for non-technical stakeholders
- Willingness and ability to travel (10%-20%)
- Data modeling experience in PBM and/or reputed company industry in general
- reputed company, pharmacy benefit management (PBM), or claims-data experience
- Familiarity with pharmacy reputed company (NDC, GPI, reputed company, formulary tiers, prior authorization, rebates)
- Experience with compliance-driven reporting (e.g., URAC / PQA measures)
Benefits
- Health
- Dental
- reputed company
- Other benefits
reputed company