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Principal Technical Product Manager (AI & Data Science)
Role Overview
We are seeking a Principal Technical Product Manager to lead the strategy, development, and scaling of enterprise AI and data science products. This role sits at the intersection of product strategy, advanced analytics, and platform engineering , driving the end-to-end lifecycle from concept through production.
As a senior individual contributor, you will operate as a product leader and technical translator , aligning stakeholders, shaping roadmaps, and ensuring delivery of high-impact AI solutions that deliver measurable business value.
Key Responsibilities
Product Strategy & Vision
- Define and own the product vision, strategy, and roadmap for AI/ML-driven products and data platforms
- Translate business objectives into scalable, reusable AI product capabilities
- Identify opportunities to leverage advanced analytics, machine learning, and generative AI to drive business impact
End-to-End Product Ownership
- Lead product lifecycle from opportunity identification → POC → MVP → production scaling
- Ensure products meet enterprise standards for architecture, governance, and compliance
- Define success metrics (KPIs, adoption, ROI) and manage performance post-launch
Technical Product Leadership
- Define technical requirements and architecture trade-offs
- Prioritize backlog and align delivery to business value
- Ensure solutions are scalable, secure, and production-ready
- Stay current on AI/ML trends and evaluate emerging technologies
Stakeholder & Cross-Functional Alignment
- Act as primary interface across business, technology, data science, and governance teams
- Lead workshops and facilitate alignment on priorities, scope, and trade-offs
- Communicate clearly with both executive and technical audiences
Execution & Delivery Excellence
- Drive agile product development , including backlog prioritization and sprint planning
- Manage dependencies across teams and ensure on-time, high-quality delivery
- Proactively identify risks and implement mitigation strategies
Governance, Risk, and Responsible AI
- Ensure solutions adhere to enterprise AI governance, security, and compliance standards
- Embed best practices for model validation, monitoring, explainability, and fairness
- Partner with risk, legal, and security teams on production readiness