Head of Data Strategy and Enablement

Marsh McLennan•New York, NY

1d•$160,200 - $288,400•Hybrid

About The Position

This leadership role drives the firm’s data strategy, enabling colleagues to benefit from the extensive data platform and derive insights that deliver value to them and clients. The role leads a team of approximately 20 experts across the US, UK, and Brazil, focusing on innovation with data technologies, including extensive use of AI capabilities, to continuously improve data-driven capabilities. There is an additional focus on client advisory through data-led reinsurance analytics to win and retain business. An emerging forward-deployed practice will support broking and analytics colleagues in advising clients, using data and AI products built on the data lake. The role involves turning learnings into new data and AI products that scale across the client base and push the frontier of Marsh Re’s data and AI work. This role is based in New York City.

Requirements

12–15+ years in data/analytics/AI
2+ years 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)

Nice To Haves

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

Responsibilities

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.
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.
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.
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.
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.
Further extend our enterprise-wide Data Governance framework that includes clear governance, ownership, and policies.
Ensure a modern, scalable cloud-native architecture based on group strategic platforms, including Azure Data Lake (ADLS), Databricks, DBT and PowerBI.
Ensure trusted data through quality and master-data management: form-wide data quality rules, and automated profiling and remediation.
Drive value delivery through analytics, MLOps, and a data-driven culture to deliver Client Advisory capabilities.
Lead with a 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.