Product Owner

Turing•New York, NY

3d•Remote

About The Position

We are looking for a Product Owner who can own outcomes, not just artifacts -- and who brings a vision for how AI and intelligent automation can fundamentally improve how back-office operations and controls work. This role blends product thinking, deep financial services operations knowledge, and hands-on experience driving AI-enabled transformation: shaping a roadmap, aligning stakeholders, planning intelligently, and driving execution with transparency. You'll partner closely with Back Office Operations and Global Business Finance, helping modernize workflows and platforms in a way that is practical, controlled, and genuinely improves how the business runs -- with AI as a core enabler. Our team has a track record of delivering ~95% of projects on time and under budget. We do that by treating ambiguity as work: we break complex initiatives into two phases -- Discovery (to distill the real problem, align stakeholders, and establish a confident plan) and Delivery (to execute with clear milestones, risks, and dependencies).

Requirements

7+ years in a Product Owner, Product Manager, or Senior Business Analyst role within financial services. Direct experience with back-office operations, fund accounting, reconciliation, controller workflows, or treasury is required -- not just financial services broadly.
Demonstrated experience leading AI-driven or intelligent automation initiatives within finance or investment operations -- designing AI-enabled workflows, not just consuming AI tools. You've fundamentally changed how an operational process works through the application of ML, NLP, LLMs, or advanced analytics.
Hands-on experience with fund accounting, reconciliation platforms, NAV processes, GL controls, or operational control frameworks at an asset manager, hedge fund, or fund administrator.
Demonstrated strength in planning, budgeting, tracking, and delivery execution -- you can run a tight ship without turning it into bureaucracy.
Strong stakeholder management skills: you build rapport, create clarity, and keep diverse groups aligned across operations, finance, technology, and leadership.
Excellent written and verbal communication; you can make complex work understandable and actionable.
Proven ability to break down complex problems and drive decisions with incomplete information.
Comfort working closely with engineering and data science teams; you don't need to code, but you should understand how software gets builds, how models work, and how AI capabilities get embedded into real workflows.
High integrity and maturity: you're transparent, consistent, and trusted.

Nice To Haves

Experience with platforms such as Geneva, Arcesium, Investran, Kyriba, or similar fund accounting, operations, and treasury systems.
Background in treasury, cash management, or investment operations transformation.
Experience working with external fund administrators and managing the validation of their deliverables.
Experience working in modern delivery environments (CI/CD, cloud platforms, event-driven systems, workflow orchestration, data platforms).
A track record of driving transformation process, platform, or operating models -- without losing the business in the process.
Familiarity with AI/ML frameworks, LLM integration patterns, or intelligent automation platforms in a financial services context.

Responsibilities

Own discovery. Lead structured discovery efforts to clarify problem statements, current-state workflows, constraints, and success metrics -- then translate that into a plan stakeholders can believe in.
Set direction. Build and maintain a product roadmap that balances transformation, near-term outcomes, and operational stability.
Drive AI-enabled transformation. Identify opportunities to embed AI, ML, and intelligent automation into operational workflows -- from reconciliation matching and exception triage to controls monitoring and evidence generation.
Work with data science and engineering to move beyond manual processes, designing workflows where AI augments human judgment rather than replacing it.
Run delivery with discipline. Define milestones, manage dependencies, track progress, and surface risks early.
Be the connective tissue. Coordinate across business users, engineering, architecture, data science, and leadership; communicate status, tradeoffs, and decisions clearly.
Bring users along. We use agile practices, but not every user group is mature in agile. You'll build trust through honest conversations, humility, and transparency -- helping partners adopt better ways of working over time.
Own the backlog. Translate needs into well-formed epics and stories with clear acceptance criteria; prioritize based on business value, risk, and effort.
Handle conflict well. Navigate competing priorities, resolve disagreements constructively, and keep teams focused on outcomes.
Champion quality and controls. Ensure solutions are operable and auditable where needed -- clear definitions, controlled rollouts, and attention to reliability.
Use AI responsibly. Leverage AI tools to move faster (analysis, writing, summarizing, prototyping, documentation), while maintaining strong judgment, validation, and appropriate guardrails. Model responsible AI usage for the team and the organization.