Data Scientist

About AffirmedRx

AffirmedRx aims to enhance healthcare outcomes by creating transparency, integrity, and trust within pharmacy benefit management. The company focuses on simplifying pharmacy benefits, making them straightforward to access, and prioritizing the best interests of employers and the individuals they affect. This is achieved by ensuring clarity in business practices, emphasizing clinical methodologies, and leveraging advanced technology.

Join us to transform healthcare outcomes for everyone. We dedicate ourselves to always doing what is right.

Position Summary

The Data Scientist specializing in AI and Machine Learning is responsible for designing, developing, and validating sophisticated analytics that transform pharmacy, claims, clinical, and member data into actionable decisions. This role entails full ownership of machine learning models, utilizing AI and natural language processing on unstructured clinical and member data, resolving member identities across disparate data systems, and delivering results via tools and dashboards accessible to the business. The position bridges data science, clinical/pharmacy reporting, and applied AI, collaborating closely with data engineering, clinical teams, reporting, and client success.

Key Responsibilities

• Construct predictive and prescriptive machine learning models to forecast pharmacy costs and risks, including techniques for feature engineering, model selection, and interpretability such as SHAP-based feature attribution.
• Develop risk and comorbidity scoring at the member level by mapping drug identifiers (e.g., NDC to ATC) to clinical conditions and severity weights, ensuring validation against edge cases.
• Automate labor-intensive clinical operations like prior-authorization override processes by transitioning manual workflows into rules- and model-driven pipelines.
• Apply AI and NLP methods to analyze unstructured clinical and member text data, including sentiment analysis, topic modeling, and tokenization of open-response surveys and feedback.
• Employ AI tools like large language models and internal AI services to automate clinical policy and documentation tasks, designing effective prompts and validating outputs while controlling risks like hallucinations or format inconsistencies.
• Contribute to shaping the organization's AI strategy through model evaluation, creating evaluation datasets, and implementing monitoring systems for model drift and validation.
• Design and maintain robust fuzzy matching algorithms to uniquely identify members across carriers and systems, managing challenges like collisions, cluster analyses, and implementing auditing and logging mechanisms.
• Monitor matching accuracy and reduce errors or fragmentation by documenting data lineages and implementing safeguards against duplicate records.
• Generate clinical and pharmacy analytics, such as medication adherence and persistence metrics at drug, class, and NDC levels, aligned to compliance standards like URAC and PQA.
• Perform quality assurance and validation of reporting tools such as pharmacy trend dashboards and per member per month metrics, reconciling discrepancies between source data systems.
• Develop analytical prototypes and tools, including user-friendly front ends like Streamlit applications, for use by sales, clinical, and pricing teams.
• Collaborate with data engineering to deliver validated datasets into warehouses and support moving prototypes to production.
• Ensure comprehensive QA and validation of all analytic outputs, including auditing claims files and validating model outcomes prior to release.
• Document all models, logic, data sources, update schedules, and troubleshooting protocols to ensure reproducibility and transparency.
• Work cross-functionally with clinical, reporting, pricing, client-success, and engineering teams to gather requirements and translate them into analytical solutions.

Required Qualifications and Skills

• A degree in a quantitative discipline such as data science, statistics, computer science, or applied mathematics, or equivalent experience.
• Two to three years of practical experience applying Python and SQL for data analysis, machine learning, natural language processing, data quality, and record linkage projects; prior healthcare or pharmacy benefit management data modeling experience is advantageous.
• Strong proficiency in Python data science libraries (e.g., pandas) and machine learning frameworks, alongside solid SQL skills.
• Proven track record in building, validating, and explaining machine learning models including advanced feature engineering techniques.
• Experienced with NLP methods like sentiment analysis and topic modeling, and skilled in deploying AI/LLM tools in production workflows with rigorous output validation.
• Expertise in entity resolution and probabilistic record matching to maintain data quality.
• Comfortable working with cloud-based data warehouses and data lakes, collaborating effectively with data engineering teams for production deployments.
• Familiarity with healthcare datasets, especially related to pharmacy benefit management and claims data.
• Knowledge of pharmacy data structures and concepts such as NDC, GPI, ATC codes, formulary tiers, prior authorization, and rebate processes.
• Experience conforming to compliance-driven reporting requirements, including URAC and PQA measure standards.
• Skill in creating analytical dashboards or front-end tools for stakeholders with varying technical backgrounds using tools like Streamlit or other BI platforms.
• Willingness and capability to travel occasionally, approximately 10 to 20% of the time.

Benefits and Work Culture

• Opportunity to influence transformative changes in pharmacy benefit management while enhancing patient care outcomes.
• Work in an environment where team wellbeing is prioritized, supporting individual and collective success.
• Competitive pay structures including comprehensive health, dental, vision, and other benefits.

Additional Information

AffirmedRx is an equal opportunity employer dedicated to creating a diverse and inclusive workplace. Remote employees must ensure their workspaces are professional and free from distractions to maintain high levels of performance and collaboration.