Innovation Product Strategist

Brown Brothers Harriman•Boston, MA

19h•$100,000 - $155,000•Hybrid

About The Position

At BBH, Partnership is more than a form of ownership—it’s our approach to business and relationships. We know that supporting your professional and personal goals is the best way to help our clients and advance our business. We take that responsibility seriously. With a 200-year legacy and a shared passion for what’s next, this is the right place to build a fulfilling career. About the Team Our mission is to reinvent how financial services firms build, govern, and run data integrations and transformations. Today, that work is fragmented across teams, tools, and handoffs—slow to deliver, expensive to maintain, and painful to change. We’re collapsing it into a single AI-native platform that empowers business users to self-serve (without writing a ticket) while giving engineering teams the governance, security, and auditability they require. We’re growing our founding team now—high ownership, high velocity, and real customer impact. About the Role We are seeking a Product Strategist to accelerate the delivery, quality, and adoption of a GenAI-native platform for the financial services industry. This is a hands-on, high-ownership opportunity where you’ll help bring a 0-to-1 product vision to life—turning new capabilities into real workflows that teams can trust and adopt. You’ll work day-to-day with product management, engineering, data, and customer teams to turn prioritized use cases into clear requirements, shippable increments, and production-ready capabilities—balancing speed, trust, and operational rigor.

Requirements

5+ years in product operations, business analysis, or delivery support within financial services operations and/or data operations (e.g., ETL, reconciliations, data quality, file-based processing)
Hands-on experience in agile delivery (sprint planning, backlog refinement, demos, retrospectives)
Excellent written communication and documentation skills (requirements, workflows, test artifacts)
Comfort collaborating with both technical and non-technical stakeholders; able to translate between business outcomes and implementation details
Analytical mindset and strong attention to detail; able to spot gaps, risks, and inconsistencies before they become defects
Strong stakeholder management—you can say “no” (or “not yet”) diplomatically while maintaining trust and momentum
Comfort with ambiguity—requirements evolve; you create clarity rather than wait for it
Customer empathy—genuine curiosity about user needs and workflows, not just managing a backlog

Nice To Haves

Exposure to AI, analytics, or data-driven products (GenAI use cases, ML features, dashboards, experimentation)
Understanding of financial messaging and data exchange patterns (e.g., SWIFT, file transfers via FTP/SFTP)
Familiarity with capital markets data domains and platforms (e.g., ABOR/IBOR)
Exposure to testing/QA practices (test cases, UAT support, defect triage)

Responsibilities

Own and govern a prioritized feature portfolio—making tradeoffs explicit and keeping delivery focused
Translate problem statements into crisp requirements (user stories, acceptance criteria, edge cases) for design and engineering
Partner with product leadership to continuously refine prioritization based on customer feedback, delivery learnings, and risk
Maintain and communicate the roadmap, ensuring dependencies, milestones, and release readiness are visible
Drive sprint planning, backlog refinement, and release readiness to keep teams shipping predictably
Be the day-to-day point of clarity for engineers and designers—answering questions, resolving edge cases, and documenting decisions
Coordinate UAT and rollout validation, ensuring what we ship meets user expectations and operational constraints
Support user and client onboarding, ensuring teams reach value quickly and consistently
Contribute to training materials, release notes, and documentation that scale product understanding
Build and maintain a living knowledge base (workflows, FAQs, decisions) that reduces friction for users and internal teams
Support data creation and preparation activities that improve model performance and product reliability
Create and curate structured knowledge assets that power GenAI experiences (taxonomy, reference data, examples)