Product Manager – Investigation management - data42

Major accountabilities:

• Define and communicate the Investigation Management product vision and roadmap, balancing automation with user functionality, and release management (e.g., Investigation request form, UX improvements, LLM-assisted suggestions).
• Engage end users (researchers, GPHs, data scientists, data engineers, data explorers) to gather requirements, validate personas, and prioritize features via the product council based on scientific impact and operational feasibility.
• Lead cross-functional delivery across engineering and UX to:
• • Implement LLM-enabled capabilities for form completion, analogy-based scope suggestions, and similar-investigation recommendations.
  • Improve transparency and usability (opt-out for findings/conclusions, clearer data promises during form filling).
  • Assess and evolve application components based on end-user-derived requirements.
• Own the product lifecycle from ideation to launch and continuous improvement:
• • Scope Investigation Management release thoroughly; implement non-disruptive near-term changes where no fundamental technical issues exist.
  • Evaluate data source alternatives and risks; ensure data inventory and subset per release.
• Establish and track product metrics:
• • Adoption of investigation cycle-time, data readiness coverage, user satisfaction, and quality/traceability of findings.
  • Measure contribution of LLM features to efficiency and accuracy within compliance constraints.
• Translate scientific workflows and trial risk management needs into intuitive, governed experiences:
• • From scientific question to scoped investigation, to anonymized data browsing, to reproducible findings.
  • Support co-discovery by surfacing similar scopes and guiding users through transparent, documented decisions.
• Ensure governed, compliant access to data:
• • Align with privacy, security, and governance standards for anonymization and RWE integration.
• Consolidate and improve documentation hygiene (creating a single source of truth from fragmented Confluence content) to reduce ambiguity for end users.

Minimum Requirements:

We are looking for a strategic and hands-on Product Manager who thrives at the intersection of science, data, and technology, and is passionate about operationalizing investigation workflows at scale. You should excel at translating scientific questions and risk management needs into intuitive, governed digital experiences that balance automation with expert user control.

• Product Management Expertise:
• • Proven ability to define product vision, strategy, and roadmap for workflow-heavy products.
  • Experience coordinating delivery of modular, integrated releases and managing product councils (or equivalent governance).
• Data and Technical Acumen:
• • Experience/Familiarity in working with data platforms, data pools, anonymization, RWD integration, and multi-layer architectures.
  • Extensive working knowledge with GenAI/LLM-assisted features (form completion, scope suggestions, similarity search) and their constraints in regulated settings.
  • Understanding of data models, metadata standards and alignment, and trade-offs in switching or harmonizing sources.
• Leadership and Collaboration:
• • Skilled in influencing diverse stakeholders (GPHs, data scientists, data engineers, “data explorers”) and aligning on requirements from end-user interviews.
  • Ability to manage feedback fatigue and drive clarity on personas and value propositions.
• Scientific and Data Literacy:
• • Understanding of R&D investigation workflows, trial risk management needs, and multi-modal data readiness.
  • Ability to translate scientific questions into data requirements and governed access patterns.

Work Experience:

• Bachelor’s or Master’s degree in Life Sciences, Computer Science, Engineering, or related field.
• 5+ years of product management experience in data-driven or workflow-centric products; exposure to pharma R&D or scientific data platforms preferred.
• Track record delivering complex, cross-functional products with strong execution and change management.
• Strong analytical and problem-solving skills focused on data readiness, usability, and compliance.
• Excellent communication and stakeholder engagement abilities; experience consolidating fragmented documentation.
• Familiarity with agile methodologies and modern product management tools.

Languages :

• English.

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