Data Product Manager

About this role

Job Type: Permanent Build a brilliant future with Hiscox   Product Manager, Data Science, AI & Innovation

Position: Data Product Manager Reports To: Director of Product & Delivery, Data Organization Location: Atlanta, GA; Boston, MA; NYC Manhattan; West Hartford, CT (Hybrid)

The Team: Our Data organization creates and manages a portfolio of trusted, reusable, and governed data products that deliver measurable business value and drive profitable growth for the company. We operate an agile, product-based delivery model with cross-functional scrum teams aligned to business objectives and key results. We are in the midst of a deliberate shift toward an AI-first operating model — embedding AI, GenAI, and intelligent automation into how we discover, build, deploy, and scale data products, and this role sits at the leading edge of that shift, partnering with our data science, AI engineering, and intelligent automation teams to turn emerging capability into real business outcomes.

About the Role: We are looking for a Product Manager to own the data products and capabilities delivered by our intelligent automation, data science, and AI engineering teams. Where our core and steady-state data products keep the business running, this role is focused on what's next — defining, prioritizing, and delivering the models, automations, and AI-enabled capabilities that create new value for business stakeholders across the US value chain (marketing, operations, underwriting, claims, distribution, and finance).

This is a forward-looking, technically fluent product role for someone who understands both the art of the possible and the discipline of shipping production-grade data science, automation, and AI products. You will partner daily with data scientists, ML/AI engineers, and automation engineers to translate ambiguous business problems into well-scoped product initiatives, and you will be expected to actively drive horizon scanning — working with these teams, business stakeholders, vendors, and the broader industry to proactively anticipate, evaluate, and bring forward what's next in data science, automation, and AI so the organization stays ahead rather than reactive.

You will own the roadmap, backlog, and outcomes for this product area end to end, ensuring every automation, model, and AI capability we build is grounded in a clear business case, responsibly governed, and designed to scale.

What You'll Do:

Product Ownership, Vision & Strategy: • Define and own the vision, strategy, and roadmap for data products and capabilities delivered by the intelligent automation, data science, and AI engineering teams.

• Translate business problems and opportunities into well-defined product initiatives, with clear hypotheses, success metrics, and business cases for automation, data science, and AI investments.

• Prioritize and sequence a backlog that balances quick-win automations, applied data science use cases, and larger AI/ML product builds against business value and delivery capacity.

• Champion the organization's AI-first operating model, ensuring new data products are designed from the outset for responsible AI use, scalability, and integration with the broader data platform.

Horizon Scanning & Innovation: • Drive structured horizon scanning with the data science, automation, and AI engineering teams, business stakeholders, industry peers, and vendors to proactively identify emerging tools, techniques, and use cases.

• Evaluate emerging AI, GenAI, machine learning, and automation capabilities for applicability and value to the business, and translate promising innovations into proof-of-value and pilot proposals.

• Maintain an active view of the competitive and industry landscape in P&C insurance data science and automation, sharing relevant insights with leadership and stakeholders to inform strategy.

• Foster a culture of experimentation, running structured pilots and proofs of concept to test new capabilities before committing to full-scale investment.

• Perform hands-on data analysis and SQL-based investigation to validate data quality, troubleshoot issues, and inform product decisions.

• Manage capability-related incidents, questions, and escalations, communicating clearly with technical and non-technical audiences alike.

• Validate and sign off on innovation products, confirming accuracy, functionality, and Enterprise Data management (EDM) standards are met.

Stakeholder & Delivery Partnership: • Partner with business stakeholders across US business functions to understand their goals and identify opportunities for automation, data science, and AI to create value.

• Work closely with data scientists, ML/AI engineers, and automation developers to scope, refine, and validate requirements, acceptance criteria, and model/automation performance expectations.

• Partner with the Delivery Manager and Scrum Masters to keep the backlog refined, sequenced, and ready for delivery within agile ceremonies.

• Communicate roadmap, tradeoffs, experimentation outcomes, and innovation findings clearly to both technical teams and business/executive stakeholders.

• Collaborate with peer Product Managers, Architecture, and Governance teams to ensure new automation, data science, and AI products integrate cleanly with core and steady-state data platforms.

What Success Looks Like: Outcomes are defined to make goal-setting, Performance & Development Reviews (PDRs), and mid-year reviews straightforward: • Value Delivery — Automation, data science, and AI products in production demonstrate measurable business value against their original business case.

• Innovation Pipeline — A healthy, continuously refreshed pipeline of evaluated opportunities generated through active horizon scanning with teams and stakeholders.

• Roadmap Health — A prioritized, transparent roadmap that balances near-term automation wins with longer-horizon data science and AI investments.

• Responsible Scaling — New capabilities are des

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