Product Manager
Job Title: Senior Product ManagerContract Duration: ASAP-6 months with possible extensionLanguages Required: EnglishWork Location: Full-remote (Full-time) OverviewThe ideal candidate brings hands-on data science expertise, a strong understanding of Real World Evidence (RWE) data sources, and the ability to translate complex analytics into scalable, high-impact products supporting:Evidence generation Regulatory and HEOR needs Clinical development Safety monitoring Commercial use cases Key ResponsibilitiesProduct Strategy & OwnershipOwn the end-to-end product lifecycle for data science–driven RWE products (ideation → roadmap → delivery → adoption → value realization) Define product vision, strategy, and KPIs aligned to pharma business goals (e.g., time-to-insight, study acceleration, cost reduction) Translate clinical, medical, regulatory, and business needs into: Clear product requirements User stories Prioritization frameworks Balance short-term delivery with long-term scalability (datasets, models, tools, APIs) Data Science & Advanced Analytics LeadershipPartner with data scientists, statisticians, epidemiologists, and engineers to deliver advanced analytics and ML solutions Provide hands-on guidance and review for: Cohort design & feature engineering Predictive, causal, and descriptive modeling Model performance, validation, interpretability, and limitations Act as a bridge between technical and non-technical stakeholders, ensuring: Analytical rigor Clear, actionable insights Real-World Evidence (RWE) ExpertiseLead products leveraging diverse RWE data sources, including: Claims, EHR, pharmacy, lab, registry Genomics and digital health data Support key RWE use cases: HEOR and safety surveillance Comparative effectiveness research Regulatory submissions & post-marketing commitments Clinical trial feasibility & external control arms Commercial and market access insights Apply best practices in: Data readiness and quality Bias awareness & confounding mitigation Transparency and reproducibility Stakeholder & Customer EngagementEngage with clinical, medical affairs, HEOR, regulatory, commercial, and IT stakeholders Lead: Requirements workshops Roadmap reviews Value storytelling with senior stakeholders Serve as a trusted partner in governance, compliance, and AI/analytics discussions Governance, Compliance & QualityEnsure compliance with: GxP standards Data privacy regulations (HIPAA, GDPR) Embed: Governance Reproducibility Documentation Auditability Support internal and external audits/reviews of analytical methods Value Measurement & Continuous ImprovementDefine and track ROI and value metrics, such as: Cost avoidance Study acceleration Reusability of datasets/models Monitor: Product usage Performance Outcomes Drive continuous improvement through: User feedback Analytics Experimentation Required QualificationsExperienceexperience in data science, analytics, or product roles experience in Pharma / Life Sciences Proven experience delivering RWE or advanced analytics products in regulated environments Hands-on collaboration with data science and engineering teams Data Science & Technical SkillsStrong foundation in: Statistics and machine learning Healthcare analytics Hands-on experience with: Python and/or R (preferred) SQL and large-scale datasets ML libraries (e.g., scikit-learn, statsmodels, PyTorch, TensorFlow) Understanding of cloud platforms: AWS, Azure, GCP, Databricks RWE & Domain KnowledgeDeep understanding of RWE data structures and limitations Working knowledge of: Epidemiology Observational study design Causal inference Experience supporting regulatory-grade evidence preferred Product & Leadership SkillsStrong skills in: Roadmapping and prioritization User stories & backlog management Value articulation Ability to lead without authority in matrixed environments Excellent communication skills: Translate complex analytics → clear business insights Preferred QualificationsAdvanced degree (MS or PhD) in: Data Science, Statistics, Computer Science Epidemiology or Biostatistics Experience with: AI/ML governance & model risk management Explainable AI in healthcare Exposure to regulatory bodies (FDA, EMA, HTA) Experience building: Analytics platforms Reusable data science products or assets