Position: [Data Science Product Manager]
Here is the job description. Need strong Life Science background (Pharma/Healthcare/Biotech industry exp)
The ideal candidate brings hands-on data science expertise, a strong understanding of RWE data sources, and the ability to translate complex analytical capabilities into scalable, highimpact products that support evidence generation, regulatory, HEOR, clinical development, safety, and commercial use cases.
Key Responsibilities
Product Strategy & Ownership
- Own the endtoend product lifecycle for data science driven RWE products, from ideation and roadmap definition through delivery, adoption, and value realization.
- Define product vision, strategy, and KPIs aligned to pharma business objectives (e.g., evidence generation, time-to-insight, study acceleration, external spend reduction).
- Translate business, clinical, medical, and regulatory needs into clear product requirements, user stories, and prioritization frameworks.
- Balance shortterm delivery with longterm platform scalability (datasets, models, tooling, and APIs).
Data Science & Advanced Analytics Leadership
- Partner closely with data scientists, statisticians, epidemiologists, and engineers to design and deliver advanced analytics and ML solutions.
- Provide hands-on guidance and review for:
- Cohort design, feature engineering, and modeling approaches
- Predictive, causal, descriptive, and ML techniques applied to RWE
- Model performance, interpretability, validation, and limitations
- Act as a bridge between technical teams and nontechnical stakeholders, ensuring analytical rigor is preserved while insights are understandable.
RealWorld Evidence (RWE) Expertise
- Lead products built on diverse RWE data sources, such as:
- Claims, EHR, pharmacy, lab, registry, genomics, and digital health data
- Ensure products align to common RWE use cases:
- HEOR, safety surveillance, comparative effectiveness
- Regulatory submissions and postmarketing commitments
- Clinical trial feasibility and external control arms
- Commercial and market access insights
- Apply best practices across data readiness, bias awareness, confounding mitigation, and transparency.
Stakeholder & Customer Engagement
- Engage with clinical, medical affairs, HEOR, regulatory, commercial, and IT stakeholders to shape product direction and ensure adoption.
- Lead requirements workshops, roadmap reviews, and valuestory discussions with senior stakeholders.
- Serve as a trusted partner during governance, compliance, and audit discussions involving analytics and AI.
Governance, Compliance & Quality
- Ensure product development complies with GxP, data privacy, and regulatory expectations (e.g., HIPAA, GDPR).
- Embed governance, reproducibility, documentation, and auditability into DS products.
- Support internal and external reviews of analytical methods and outputs.
Value Measurement & Continuous Improvement
- Define and track ROI and value metrics for RWE analytics products (e.g., cost avoidance, study acceleration, reuse of datasets/models).
- Monitor product usage, performance, and outcomes to inform roadmap decisions.
- Drive continuous improvement through user feedback, analytics, and experimentation.
Required Qualifications
Experience
- 8+ years total experience in data science, analytics, or product roles, with 3 5+ years directly relevant to Pharma / Life Sciences.
- Demonstrated experience delivering RWE or advanced analytics products in a regulated environment.
- Prior experience working closely with data science and engineering teams (not just managing them).
Data Science & Technical Skills
- Strong foundation in statistics, machine learning, and analytical methods applied to healthcare data.
- Hands-on experience with:
- Python and/or R (preferred)
- SQL and large-scale analytical datasets
- Common ML and analytics libraries (e.g., scikitlearn, statsmodels, PyTorch/TensorFlow conceptual or applied)
- Understanding of cloudbased data platforms and analytics stacks (AWS, Azure, Google Cloud Platform, Databricks, etc.).
RWE & Domain Knowledge
- Deep familiarity with RWE data structures, strengths, and limitations.
- Working knowledge of epidemiology, observational study design, and causal inference concepts.
- Experience supporting regulatory-grade evidence or external stakeholder scrutiny preferred.
Product & Leadership Skills
- Strong product management fundamentals: roadmap planning, prioritization, user stories, backlog management, value articulation.
- Ability to influence and lead without direct authority across highly matrixed organizations.
- Excellent communication skills able to explain complex analytical concepts to executive and nontechnical audiences.
Preferred Qualifications
- Advanced degree (MS or PhD) in Data Science, Statistics, Computer Science, Epidemiology, Biostatistics, or related field.
- Experience with AI/ML governance, model risk management, or explainable AI in healthcare.
- Exposure to regulatory interactions (FDA, EMA, HTA bodies) involving RWE.
- Experience building internal analytics platforms, DS products, or reusable analytics assets.
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