Head of Data Strategy and Enablement

Company:

Description:

Reporting to the Chief Operating Officer, this combined commercial, product, and technical leadership role drives the firm’s data strategy, enabling colleagues to benefit from our extensive data platform, and derive insights which deliver value to them and our clients.  Leading ~20 experts across the US, UK and Brazil, the team innovates with data technologies, including extensive use of AI capabilities, to continuously improve the data-driven capabilities of Marsh Re.

There is an additional focus on client advisory: the data-led reinsurance analytics work that wins and retains business. An emerging forward-deployed practice will support broking and analytics colleagues who are advising clients, using the data and AI products we’ve built on our data lake. You will turn those learnings into new data and AI products that scale across the client base and help push the frontier of Marsh Re’s data and AI work.

This role is based in New York City.

We will rely on you for:

Client advisory

• Support analytics-led advisory that wins new business and retains clients—bringing quantitative firepower to clients’ risk and capital decisions
• Engage clients and markets with the firm’s internally built, AI-oriented products (built on our data lake), making them a reason that 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 capabilities — with the credibility of afirm that has done it (proof-by-practice, not slideware)
• Deploy engineers forward (embedded in our broking teams) to solve real problems using clients’ actual data
• Co-develop and discover new, scalable AI products with our broking, analytics and advisory teams, 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 clients and markets
• Prioritize strictly by client value, commercial impact, and feasibility — avoid one-offs that don’t generalize

Frontier data, AI & innovation platform

• Lead the firm’s frontier data and related 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
• Partner closely with GC IT to industrialize what the team provides — moving mature prototypes and IP cleanly from innovation into firm-wide production and scale
• Champion data quality, governance, security, and responsible AI.

Data Strategy & Governance

• Further extend our enterprise-wide Data Governance framework that includes:
  • Clear governance, ownership, and policies: defined data ownership, management and stewardship across the business and IT, a standardized metadata catalogue, and policies for access
  • Modern, scalable cloud-native architecture based on group strategic platforms, including Azure Data Lake (ADLS), Databricks, DBT and PowerBI
  • Trusted data through quality and master-data management: form-wide data quality rules, and automated profiling and remediation
• Value delivery: analytics, MLOps, and a data-driven culture to deliver Client Advisory capabilities as described in the next section

People & organization

• Leads with growth-oriented leadership mindset—coaching others, elevating performance through timely feedback, and building an inclusive environment that supports accountability and development.
• Lead and mentor the team — data 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 engaging with internal and external clients
• Lead the quarterly business reviews delivered to Executive Committee members as well as investment cases and roadmap trade-offs.

What you need to have:

• 12–15+ years in data/analytics/AI, 2+ in senior leadership of a
  multidisciplinary team
• Proven track record turning analytics into commercial outcomes —
  winning/retaining business or building products that do
• Demonstrated ability to build and scale data/AI products (productization, not bespoke delivery)
• Client-facing credibility — able to lead advisory engagements and engage with client and executive teams.
• 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 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)

What makes you stand out:

• 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 responsible AI lens