Head of AI Product

Role overview

BetaNXT is seeking a Head of AI Product to join its Product Management leadership team and translate the company's enterprise AI strategy into differentiated, scalable and commercially successful product capabilities delivered through DataXChange and InsightX. Reporting to the Chief Product Officer, this individual sits as a peer to BetaNXT's current Product Management leadership team, spanning Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships, co-owns the integrated product roadmap with them, and works closely with the executive leading AI strategy, technology, sales, finance, legal, risk and marketing.

The role owns product discovery, roadmap development, product requirements, commercial readiness, launch planning, adoption and performance measurement for AI capabilities across the four-layer platform: the governed data foundation, business-layer domain packs, the semantic layer and the agentic decision layer. The Head of AI Product will partner with technology leaders to convert product priorities into feasible delivery plans, coordinate with Product Management peers so AI capabilities land inside their existing roadmaps rather than as a parallel track, and establish a repeatable path from innovation and client pilots into governed production products.

Role information

Role information

Definition

Function

Product Management

Level

Senior product leadership, non-executive

Reports to

Chief Product Officer

Location

New York, NY or North Carolina, with travel as required

Scope

Client-facing AI capabilities embedded within the current Product Management roadmaps (Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships), delivered as InsightX and DataXChange

Works within Product Management alongside

The current Product Management leadership team, the heads of Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships, plus the executive leading AI strategy

Key responsibilities
• Portfolio and roadmap. Jointly maintain, with the current Product Management leadership team (Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships), an integrated AI product portfolio and 12 to 18 month roadmap covering DataXChange, InsightX (Compass, Data Studio, Solutions Hub), Val and other approved embedded or stand-alone AI capabilities, sequenced against each suite's own roadmap rather than planned in isolation.
• Product discovery and definition. Run discovery outside-in: start from market problems and buyer/user personas, not internal feature requests. Validate market problems through structured voice-of-customer interviews and win/loss analysis before writing requirements. Define target users and jobs to be done, establish product charters, translate validated market problems into requirements and acceptance criteria, and make clear prioritization recommendations grounded in market evidence rather than the loudest internal or client voice.
• Market-driven prioritization. Apply an outside-in product management discipline (Pragmatic Marketing Framework or equivalent) across the AI portfolio: maintain a current market and competitive landscape view, document distinctive competence for InsightX and DataXChange against competitors, and require a documented market problem and business case before any capability enters the roadmap.
• Delivery partnership. Work with the CTO, engineering leaders, architects and delivery teams to create realistic plans, manage dependencies, make trade-offs and maintain transparent release commitments.
• Commercialization. Develop packaging and pricing recommendations, positioning and messaging tied to validated market problems, business cases, implementation models, sales tools and buyer-facing enablement (battlecards, demo scripts, objection handling), launch plans and product-level commercial metrics in partnership with Finance, Sales and Marketing.
• Adoption and performance. Define and monitor product usage, adoption, client outcomes, service quality and financial performance. Use data and client feedback to refine priorities and improve product value.
• Cross-suite product coherence. Promote reuse of common data, platform, workflow and governance capabilities across the domain packs (Stock Record, Corporate Actions and others as they come online) and reduce disconnected or duplicative AI point solutions.
• Product Management integration. Sit in the regular Product Management leadership cadence with the heads of Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships. Represent AI capability status, dependencies and trade-offs in that forum rather than a separate one, and hold joint accountability with the relevant suite leader for any AI feature shipped inside their roadmap.
• Governance by design. Ensure product requirements include appropriate data rights, security, privacy, model documentation, explainability, Maker-Checker human oversight, monitoring, versioning, auditability and rollback capabilities, consistent with BetaNXT's model risk management standard.
• Client and market engagement. Lead detailed AI product discussions, discovery sessions and demonstrations. Support major client, sales, board and investor discussions with product-specific content as requested.
• Product organization. Build and lead a focused AI product team as approved, including product managers or product operations roles, staffed and reviewed within Product Management's existing structure rather than as a separate reporting line. Partner with technology leadership for forward-deployed engineering and other technical capacity.
• Partner evaluation. Evaluate vendors, integrations and potential partnerships from a product perspective and provide recommendations to the appropriate executive decision-makers.

Qualifications and experience
• 10 or more years of relevant experience in enterprise product management, financial technology, data products, analytics products or adjacent fields.
• Demonstrated experience taking AI, machine learning, data or workflow products from discovery through production launch and measurable adoption.
• Strong understanding of regulated financial-services workflows, with wealth management, securities processing, investor communications, tax, fund data or operations experience preferred.
• Experience developing roadmaps, product requirements, business cases, pricing or packaging recommendations and launch plans for enterprise software or data products.
• Trained in and practiced with an outside-in product management discipline such as the Pragmatic Marketing (Pragmatic Institute) Framework: market problems, buyer personas, win/loss analysis and distinctive competence driving the roadmap, rather than internal or engineering-led feature requests.
• Ability to work effectively with engineering, architecture, data, security, risk, legal, sales and operations teams in a matrixed environment, including co-planning roadmaps with peer product leaders who own their own suite's priorities and delivery commitments.
• Sufficient technical fluency to understand AI and data-platform trade-offs, model limitations, integration patterns and production-readiness requirements without acting as the enterprise architect.
• Strong client presence and the ability to explain complex AI and data capabilities to business, product and technical audiences.
• Experience leading product managers or multidisciplinary product teams and developing accountable, outcome-oriented operating practices.
• Bachelor's degree or equivalent experience required; advanced degree preferred but not required.

Leadership attributes
• Execution-oriented and comfortable moving from ambiguity to clear decisions, plans and measurable outcomes.
• Commercially minded, with strong judgment about where AI creates genuine client value versus unnecessary complexity.
• Collaborative, able to operate as a peer inside Product Management, share roadmap ownership with other suite leaders, and influence teams without relying on formal authority.
• Disciplined about scope, risk, evidence and production quality while maintaining urgency and pace.
• Credible with senior clients and internal leaders, but willing to remain close to product detail and delivery.