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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading product vision and strategy for AI/ML and data platform products, with a strong focus on portfolio management, agile methodologies, and stakeholder engagement. Proficient in managing the end-to-end product lifecycle, ensuring compliance, and driving data governance initiatives.
Highest-signal resume keywords
Product Management ExperienceAI/ML Product Lifecycle ManagementAgile Delivery MasteryData Platform ExpertiseExecutive Communication Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data MeshLakehouse ArchitectureMLOpsSQLPythonPortfolio PrioritisationData QualitySelf-Service AnalyticsModel EvaluationPrompt Engineering
Soft Skills
Stakeholder EngagementLeadershipStrategic ThinkingInfluencing SkillsCollaboration
Tools & Technologies
JiraConfluenceProductboardMiroTableauPower BIAzure SynapseDatabricksDelta LakeDbt
Certifications & Qualifications
Formal PM Certifications
Industry Keywords
Regulatory ComplianceResponsible AIPharma/Life SciencesHealthcareData Governance
Tech Stack
Tools & technologiesAzureCloudPythonSQLTableau
About the role
Key responsibilities & impact- Set multi-year product direction and shape platform strategy alongside the Director and Principal Engineers
- Lead a team of Product Managers and Business Analysts
- Own and drive product vision and roadmap across data platform and AI/ML products
- Partner with engineering leadership to align product strategy with enterprise data architecture and business priorities
- Lead portfolio prioritisation across concurrent workstreams, technical debt, new capabilities, and regulatory compliance
- Assess emerging technologies including data mesh, GenAI, advanced RAG, and multi-agent orchestration
- Engage Director- and VP-level stakeholders across R&D, Supply Chain, Commercial, and IT
- Lead executive roadmap reviews, investment proposals, and architecture governance forums
- Build relationships with data domain leads, AI engineering leaders, and data scientists
- Own the end-to-end AI/ML product lifecycle, including model tooling, MLOps/LLMOps pipelines, GenAI applications, RAG systems, and multi-agent frameworks
- Define AI-ready data product requirements, embeddings, feature stores, versioned training data, and evaluation frameworks
- Champion Responsible AI practices, bias mitigation, explainability, and regulatory compliance
- Own data platform products including ingestion pipelines, lakehouse layers, cataloguing, lineage, data quality, and self-service analytics
- Lead analytics and BI product development for Tableau, Power BI, and programmatic analytics
- Drive data governance product strategy covering metadata, classification, access control, and compliance tooling
- Own backlog health across engineering squads and drive agile ceremonies
- Define and report platform KPIs for adoption, AI quality, pipeline reliability, time-to-insight, delivery velocity, and stakeholder satisfaction
Requirements
What you’ll need- Bachelor’s or Master’s in Computer Science, Data Engineering, Business Analytics, Information Systems, or a related field
- 8+ years of product management experience, including at least 4 years on data platforms, AI/ML products, or enterprise analytics infrastructure
- Experience owning a multi-product portfolio and leading Product Managers or Business Analysts
- Portfolio-level prioritisation, high-stakes trade-off decisions, and OKR-driven roadmapping across multiple engineering squads
- Mastery of agile delivery and tooling including Jira, Confluence, Productboard, and Miro
- Ability to produce strategy documents, business cases, roadmaps, and PRDs
- Experience with lakehouse architecture, data mesh, batch and streaming pipelines, data cataloguing, lineage, data quality, and self-service analytics
- Experience with Azure Synapse, Databricks, Delta Lake, and dbt
- Experience with BI tooling including Tableau, Power BI, and semantic layers
- Ability to read and reason about SQL and Python
- Experience with the end-to-end ML lifecycle, MLOps, LLMOps, RAG architectures, vector databases, and multi-agent systems
- Experience managing GenAI or LLM-powered products in production, including prompt engineering, model evaluation, hallucination mitigation, and responsible AI integration
- Experience with executive communication, board-ready decks, investment proposals, and technical and business narratives
- Experience influencing at Director/VP level and working across distributed, matrixed organisations
- Preferred: advanced AI product experience, data mesh, regulatory experience, pharma/life sciences or regulated healthcare background, open-source AI/ML or data community involvement, formal PM certifications, SAP landscapes, SAP Business Data Cloud, and ECC-to-S/4HANA transformation programmes
Benefits
Comp & perks- Comprehensive benefits program for US employees (details referenced in GSK US Benefits Summary)
- Equal Opportunity Employer