Director, Data Product & Analytics Engineering – Pathway AI

Role Summary
- Accountable for developing, owning, and delivering reusable data product and analytics engineering foundations for the enterprise Patient FIND capability.
- Translate Patient FIND strategy into governed, reusable data products, analytical workflows, feature logic, quality controls, lineage, and measurement infrastructure supporting AI/ML, CRM/field activation, BI, omnichannel workflows, and executive decision-making.

Key Responsibilities
- Develop and own Patient FIND technical requirements framework (data products, analytical logic, data flows, measurable outcomes).
- Maintain integrated view of use cases, source data, shared layers, dependencies, risks, and delivery priorities.
- Identify gaps in signals/data quality/processes and recommend scalable solutions.
- Develop and deliver governed Patient FIND data products across claims, EHR/EMR, lab, Rx, CRM, specialty pharmacy, digital engagement, and other approved sources.
- Establish reusable standards (feature logic, cohort definitions, metadata, lineage, quality controls, documentation) and enforce reuse vs. one-off builds.
- Define analytical data foundation for patient identification, segmentation, HCP prioritization, predictive modeling, treatment progression, adherence, and retention.
- Enable AI/ML teams (feature engineering, model input design, validation, monitoring, model-enablement practices).
- Own signal readiness framework and implement quality/governance/access/consumption standards.
- Lead resolution of privacy, compliance, governance, and access issues.
- Define deployment/measurement framework across CRM, field, marketing, BI, medical, omnichannel, and leadership.
- Own measurement layer for closed-loop learning linking identification, engagement, outcomes, and model refinement.

Required Qualifications
- Bachelor’s degree in Data Science, CS, Engineering, Statistics, Mathematics, Bioinformatics, Health Informatics, Epidemiology, Life Sciences, or related quantitative field.
- 8+ years progressive experience in data science/analytics engineering/healthcare analytics/data product development/life sciences analytics or related technical leadership.
- Demonstrated leadership of complex cross-functional data/analytics initiatives from need to delivery and adoption.
- Strong SQL and Python; experience transforming/profiling/validating/analyzing large-scale healthcare/life sciences data.
- Experience building reusable analytical datasets, feature logic, model inputs, data products, measurement layers, and production-grade workflows.
- Experience with healthcare/pharma data (claims, EHR/EMR, labs, Rx, CRM, specialty pharmacy, hub/patient services, digital engagement, or real-world data).
- Strong understanding of data quality, metadata, reproducibility, lineage, access controls, privacy, governed analytics, and AI/ML enablement.

Preferred Qualifications
- Experience in pharma/biotech/rare disease/specialty pharmacy, commercial or medical analytics, RWD, HEOR, or patient identification/patient finding.
- Familiarity with modern analytics/data platforms (Databricks, Snowflake, Azure, AWS, Dataiku, dbt, Airflow, Git, Power BI, Tableau).
- Familiarity with MLOps/model monitoring/CI-CD/API data products.
- Experience with commercial pharma use cases (HCP targeting, patient finding, patient journey, adherence, treatment switching, territory/account prioritization, launch or field effectiveness).
- Experience in highly regulated environments with privacy/compliance/legal/medical/governance stakeholders.

Application instructions
- Not provided.