AI Product Management, VP II — State Street Investment Management

State Street Investment Management is hiring an AI Product Manager (Vice President level) to sit at the intersection of asset management operations and applied AI. This isn't a research role — it's a delivery-focused seat responsible for turning real investment, trading, and operations pain points into AI features that actually ship and get adopted across the firm.

You'll work inside the Data, Analytics and AI Services group, acting as the translator between business stakeholders who understand portfolios and workflows, and engineering teams who understand models and infrastructure. Expect a mix of roadmap ownership, competitive scanning, and hands-on delivery management across onshore and offshore teams.

Why this seat is different from a typical "AI PM" opening: it sits inside a global asset manager, not a standalone tech company, which means every model or workflow you ship has to hold up against real portfolios, real trading books, and real regulatory scrutiny. You'll be judged less on how novel the AI is and more on whether it holds up in production and gets genuinely adopted by the investment, product, client, finance, and operations teams who have to live with it every day.

Key Responsibilities

• Sit with investment, product, client, finance, and operations stakeholders to map current workflows and surface where AI can remove friction or manual effort.
• Turn business problems into reusable AI product lines and feature sets rather than one-off fixes — the goal is capability that scales across teams, not a single custom build.
• Track what competitors, big tech, and fintech players are shipping in AI, and feed that intelligence back into the product roadmap.
• Run Agile delivery end-to-end: prioritize the backlog, manage milestones, and push release cycles from concept to production faster.
• Review output from AI engineering, data science, and financial engineering teams and give the kind of specific, fast feedback that speeds up iteration instead of stalling it.
• Present new capabilities to stakeholders, explain what shipped and why it matters, and manage expectations as priorities shift.
• Coordinate daily execution across onshore and offshore AI engineering, data science, and financial engineering pods, keeping communication and prioritization tight.

What You'll Need

• A master's degree in computer science, engineering, finance, or a related quantitative field.
• 15+ years in asset management, with real exposure to at least one of: portfolio management, trading, sales, client relationship management, product research, finance, or operations.
• 10+ years of hands-on product management specifically inside asset management, having taken products through a full lifecycle from concept to production.
• 6+ years of AI engineering or adjacent experience — working knowledge of machine learning, generative AI, and agentic AI, not just familiarity with the terms.
• 5+ years leading technology-enabled transformation programs inside complex, cross-functional organizations.
• Direct, hands-on experience designing multi-agent workflows, including both human-on-the-loop command patterns and fully autonomous agentic flows.
• Comfort with modern AI platforms such as Google Gemini Enterprise and AWS Bedrock, plus working proficiency in Python and SQL. Familiarity with AI-assisted development tools such as Claude Code and GitHub Copilot is expected.

Nice to Have

• Prior experience specifically titled or scoped as an AI Product Manager inside a bank or financial institution.
• CFA Level II completed, or an equivalent certification.

Expect a genuinely cross-functional day: one meeting with a portfolio manager describing a reporting bottleneck, the next with a financial engineer scoping a model, the next presenting a release milestone to senior leadership. The role rewards someone who can hold both the business narrative and the technical detail at once — and who's comfortable driving delivery across onshore and offshore pods without losing momentum.

If you've spent a career translating "the business wants X" into a shipped product, and you're now doing that same translation for AI specifically, this seat is built for exactly that person. It's a VP-level role with real scope: you're not asking permission to build AI capability — you're the one deciding what gets built next.