Head of Data, AI & Innovation

Job Description

Job Description

Head of Data, AI & Innovation

**If interested please send a resume to View email address on ziprecruiter.com

NYC area hybrid

$350k - $500k total comp

The role. Own the firm’s data, AI & innovation agenda and lead the ~25-person team behind it. The primary mandate is client advisory—the technology-led reinsurance analytics work that wins and renews business. You will lead the forward-deployed practice that advises clients with the AI products we’ve built on our data lake and helps them build their own data and AI strategy. You will turn those learnings into new data/AI products that scale across the client base, and you'll guide GC's frontier data and AI work. A combined commercial, product, and technical leadership role, reporting directly to the executive team.

The responsibilities.

Client advisory & commercial leadership

• Direct the analytics-led advisory that wins new business and renews accounts — bringing quantitative firepower to clients’ risk and capital decisions — and own the commercial outcomes (pipeline, wins, and retention).

• Engage clients and markets with the firm’s internally built, AI-oriented products (built on our data lake), making them a reason clients engage and stay.

• Advise clients on building their own data and AI strategy: assess where they are, design the target operating model and roadmap, and help them stand up scalable AI capability — with the credibility of a firm that did it on itself first (proof by practice, not slideware).

• Deploy engineers forward (embedded in client teams) to solve real problems on the client’s own data and earn trusted-advisor standing.

• Co-develop and discover new, scalable AI products with clients and markets, shaping high-value use cases into repeatable solutions.

• Product innovation that scales a deliberate feedback loop: turn what forward deployed teams learn in the field into productized data/AI capabilities that scale across the client base — build once, deliver to many.

• Own the data-product lifecycle (discovery → delivery → launch → continuous improvement); build revenue-generating products and scale them across customers and markets.

• Prioritize ruthlessly by client value, commercial impact, and feasibility — avoid one-offs that don’t generalize.

Data, AI & innovation platform

• Set the agenda for the firm’s frontier data and AI work: net-new capability, applied AI/GenAI, and the data foundations that new models depend on — keeping the firm ahead of the market.

• Advance machine learning, predictive analytics, and applied GenAI products from research into production; decide where to build, buy, or partner.

• Partner closely with IT to industrialize what the team proves — moving mature prototypes and IP cleanly from innovation into firm-wide production and scale, so that what you build strengthens, rather than disrupts, day-to-day operations.

• Champion data quality, governance, security, and responsible AI for the capabilities you stand up.

Strategy & executive partnership

• Define and own the data, AI & innovation strategy, tied to firm revenue and growth.

• Represent the function to executive leadership — quarterly business reviews, strategic plans, investment cases, roadmap trade-offs.

• Translate capability into value for non-technical executives and client teams.

People & organization

• Lead, hire, and mentor the team — data engineers operating as forward-deployed (client- and innovation-facing) engineers, alongside data scientists, software developers, and product managers — managing technical leads and product managers.

• Build a culture that pairs engineering rigor with commercial and product instinct, and is comfortable embedding with clients in ambiguity.

The r equirements.

• 12–15+ years in data/analytics/AI, 2+ in senior leadership of a multidisciplinary team.

• Proven track record turning analytics into commercial outcomes — winning/renewing business or building products that do.

• Demonstrated ability to build and scale data/AI products across many customers(productization, not bespoke delivery).

• Client-facing credibility — able to lead advisory engagements and embed with client and executive teams, not only run an internal function.

• Strong command of data architecture and pipelining, ML algorithms, and applied AI — technical enough to lead the technical leads.

• Experience managing both engineering leads and product managers, and setting and running a roadmap.

• A fast study — proven ability to master complex, unfamiliar domains quickly and operate credibly alongside subject-matter experts.

• Excellent executive communication and stakeholder management.

• Advanced degree in a quantitative field (or equivalent demonstrated experience).

Preferred qualifications.

• Experience in insurance, reinsurance, or financial services.

• Background at a major technology company, high-growth startup, or data/AI consultancy.

• Experience with a forward-deployed / embedded client-delivery model.

• Hands-on experience with LLM/GenAI in production, with a responsible-AI lens.