Director, Data Product & Analytics Engineering – Pathway AI

• Develop, own, and be accountable for the technical requirements framework for Patient FIND, translating business, scientific, and strategic priorities into executable data products, analytical logic, data flows, and measurable outcomes.
• Own and maintain an integrated view of Patient FIND use cases, source data, shared data layers, technical dependencies, implementation risks, and delivery priorities.
• Identify gaps in signals, data quality, processes, and technical approaches, and recommend scalable solutions that improve reuse, governance, and business impact.
• Lead technical translation and alignment across business, scientific, engineering, analytics, AI/ML, governance, and activation teams to convert ambiguity into executable plans.

• Develop, own, and deliver governed Patient FIND data products and analytical datasets across claims, EHR/EMR, lab, Rx, CRM, specialty pharmacy, digital engagement, and other approved data sources.
• Establish the reusable data product foundation and standards for feature logic, cohort definitions, metadata, lineage, quality controls, and analytical documentation.
• Ensure Patient FIND use cases build upon reusable assets and enterprise design patterns rather than creating one-off solutions.
• Define and execute a sequenced technical roadmap that prioritizes foundational capabilities, accelerates implementation, and supports long-term scale.

• Define and deliver the analytical data foundation required for patient identification, segmentation, HCP prioritization, predictive modeling, treatment progression, adherence, retention, and other Patient FIND use cases.
• Guide AI/ML teams on feature engineering, model input design, validation, monitoring, and scalable model-enablement practices.
• Provide technical leadership on tradeoffs involving model performance, governance, data quality, explainability, and downstream activation.

• Own the signal readiness framework for Patient FIND data assets and platform flows, ensuring required signals can be captured, linked, governed, and activated.
• Define and implement quality, governance, metadata, lineage, access, and consumption standards embedded within Patient FIND data products and workflows.
• Lead resolution of cross-functional data, platform, privacy, compliance, governance, and access issues, proactively surfacing risks and recommendations.

• Define and own the deployment and measurement framework for Patient FIND outputs across CRM, field, marketing, BI, medical, omnichannel, and leadership workflows.
• Own the measurement data layer that connects patient identification, engagement, outcomes, and model refinement into a closed-loop learning capability.
• Ensure outputs are consumable, measurable, scalable, and actionable while providing leadership visibility into capability maturity, constraints, and investment opportunities.

• Serve as the senior technical operating lead for the Patient FIND data product and analytics engineering capability.
• Drive alignment on technical priorities, shared definitions, quality expectations, governance requirements, and implementation approaches across Patient FIND stakeholders.
• Influence senior stakeholders and technical teams by translating complex technical and analytical tradeoffs into clear options, recommendations, and decision implications.
• Represent data product, analytics engineering, and technical readiness in roadmap, governance, investment, prioritization, and operating model discussions.

• Help define the future operating model, organizational structure, and capability roadmap required to scale Patient FIND across the enterprise.
• Serve as a player-coach, mentoring technical contributors and establishing standards, review practices, documentation, and delivery disciplines that raise capability maturity.
• Support future hiring, talent development, and succession planning for data product, analytics engineering, and AI/ML capabilities.

• Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Bioinformatics, Health Informatics, Epidemiology, Life Sciences, or related quantitative field.
• 8+ years of progressive experience in data science, analytics engineering, healthcare analytics, data product development, life sciences analytics, or related technical leadership roles.
• Demonstrated experience leading complex, cross-functional data or analytics initiatives from ambiguous business need through technical design, delivery, governance, and downstream adoption.
• Strong SQL and Python capability, with experience transforming, profiling, validating, and analyzing large-scale healthcare or life sciences data.
• Experience developing reusable analytical datasets, feature logic, model inputs, data products, measurement layers, and production-grade analytical workflows.
• Experience working with healthcare or pharmaceutical data sources such as claims, EHR/EMR, lab data, Rx data, CRM, specialty pharmacy, hub/patient services, digital engagement, or real-world data.
• Demonstrated ability to set technical direction, influence senior stakeholders, and align matrixed teams around reusable data and analytics patterns.
• Strong understanding of data quality, metadata, reproducibility, lineage, access controls, privacy considerations, governed analytics, and AI/ML enablement.
• Proven ability to translate ambiguous strategic questions into structured technical requirements, build plans, tradeoffs, and recommendations.
• Strong executive communication skills, including the ability to explain data logic, technical risks, delivery options, and business implications to technical and non-technical audiences.
• Experience mentoring, guiding, or providing technical direction to data scientists, analytics engineers, contractors, or matrixed technical contributors.

• Experience in pharma, biotech, rare disease, specialty pharmacy, commercial analytics, medical analytics, RWD, HEOR, or patient identification and patient finding.
• Experience with Databricks, Snowflake, Azure, AWS, Dataiku, dbt, Airflow, Git, Power BI, Tableau, or similar modern data and analytics platforms.
• Familiarity with MLOps, model monitoring, CI/CD, API based data products, or production grade analytics workflows.
• Experience supporting commercial pharma use cases such as HCP targeting, patient finding, patient journey analytics, adherence, treatment switching, territory or account prioritization, launch analytics, or field effectiveness.
• Experience working in a highly regulated environment with privacy, compliance, legal, medical, and governance stakeholders.
• Familiarity with rare disease commercialization, diagnostic odyssey, claims or EHR signal detection, HCP network mapping, or patient activation pathways.
• Ability to work across technical teams and business teams, especially where ownership, definitions, and decision rights are still being clarified.

• Strategic Technical Ownership: Develops and owns the technical approach that turns enterprise strategy into reusable, governed, scalable data and analytics capability.
• Senior Operator Mindset: Moves ambiguous priorities into executable plans, clear accountabilities, quality standards, and measurable delivery.
• Connective Technical Leadership: Keeps business strategy, data engineering, data science, BI, governance, platform, and activation teams connected through one coherent technical design.
• Applied Data Strategy: Understands that value comes not only from building data assets, but from making them reusable, governed, measurable, explainable, and decision-ready.
• Data Product Leadership: Establishes reusable data product patterns, technical standards, documentation expectations, and consumption pathways that can scale across brands and use cases.
• Hands-On Technical Credibility: Builds, validates, documents, and improves analytical datasets, feature logic, model inputs, quality checks, lineage, and reusable workflows.
• Healthcare Data Fluency: Understands the strengths, limitations, linkage considerations, and appropriate use of patient-level and HCP-level healthcare data sources.
• Analytical Rigor: Brings disciplined thinking to cohorts, definitions, features, quality checks, validation, performance measurement, and continuous improvement.
• Business Translation and Executive Communication: Converts complex data and technical work into clear options, tradeoffs, recommendations, and decision implications.
• Governed Innovation: Moves quickly while embedding privacy, compliance, access, explainability, metadata, and responsible AI expectations into the work.
• Enterprise Mindset: Designs for repeatability, reuse, and scalability across brands, geographies, disease areas, and future Patient FIND use cases.
• Talent and Capability Building: Mentors technical contributors, establishes standards and playbooks, supports future hiring and onboarding, and helps build the foundation for a high-performing Patient FIND data and analytics team.

Note: This description is not intended to be all-inclusive, or a limitation of the duties of the position.

It is intended to describe the general nature of the job that may include other duties as assumed or assigned.

Equal Opportunity Employer/Veterans/Disabled An Equal Opportunity Employer.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.