Company:

Marsh

Description:

The Product Analytics Lead will own the analytics strategy for Client Renewals & Broker Insights, partnering closely with brokerage, client executives, product, engineering and risk teams to improve renewal placement outcomes for clients. This role combines hands-on analytics expertise with leadership, product and delivery ownership: you will set the analytics roadmap, lead a small team of analysts/data scientists, ensure models and tooling are production-ready and governable, and translate broker workflows into high-impact, operationalised analytics products that drive broker decisions during renewals.

Primary responsibilities

• Define and own the analytics roadmap and priorities for Renewals & Broker Insights in alignment with product and commercial objectives; translate business outcomes into measurable success criteria and OKRs.

• Lead discovery with brokers, client executives and product stakeholders to identify high-value renewal problems and articulate analytics-led solutions and adoption plans.

• Oversee and contribute to data preparation, feature engineering and model development; ensure reproducibility, robust validation, calibration and explainability of models used in broker guidance.

• Architect and operationalise analytics solutions: collaborate with engineering/MLops to define data contracts, API specifications, acceptance criteria, deployment pipelines and monitoring/alerting for production models.

• Own end-to-end model governance and risk responsibilities for the area: documentation, data lineage, bias/fairness checks, regular model reviews and compliance with privacy and data access policies.

• Translate model outputs into broker-facing products: prioritise and sponsor dashboard builds, wireframes and iterative MVPs with BI/UX; ensure insights are actionable and drive measurable broker behaviour change.

• Drive adoption: produce playbooks, run pilot programmes, enable brokers through training and feedback sessions, and embed analytics into renewal workflows.

• Manage and develop the analytics team: recruit, mentor and set objectives; allocate work across projects and establish best-practice standards for code, testing and CI/CD.

• Define and own success metrics and SLAs for analytics outputs (e.g., model performance targets, adoption KPIs, time-to-insight) and report progress to senior stakeholders.

Expected outputs and deliverables

• Roadmap and prioritised backlog for analytics products supporting renewals.

• Production-grade models and inference services with documented performance.

• Broker workbench deliverables: production dashboards, decision playbooks, and integration specifications for broker workflows.

• Team artefacts: reproducible notebooks, feature & data dictionaries, ETL specifications, CI/CD pipelines and runbooks.

• Adoption and impact reports demonstrating uplift in renewal placement outcomes, broker usage metrics and ROI of analytics initiatives.

Collaboration & stakeholder interactions (day-to-day)

• Executive & Commercial stakeholders: present roadmap and impact, secure buy-in and prioritisation, and escalate risks or blockers.

• Brokers: lead discovery and pilot programmes, gather qualitative feedback and champion analytics adoption in renewal conversations.

• Product & Engineering: partner on delivery scope, define acceptance criteria and production handover; participate in sprint planning and release reviews.

• BI/UX: collaborate on dashboard design and ensure insights are interpretable and aligned to broker workflows.

• Risk / Governance / Legal: be the accountable analytics lead for governance requirements, model approvals and privacy constraints.

Required tools, technologies and technical proficiencies (levels)

• Python Advanced (production-ready scripting, pipelines, modelling libraries such as scikit-learn, xgboost/lightgbm, model explainability libraries; testable modules).

• Statistical & ML modelling Advanced (classification/regression, calibration, uncertainty quantification, causal inference desirable).

• BI & visualisation Advanced (Looker/Tableau/Power BI: design production dashboards and partner with BI engineers on delivery).

• SQL & Data warehousing Advanced (complex SQL, query optimisation and strong working knowledge of Snowflake / BigQuery / Redshift schemas and performance considerations).

• ETL / transformation Advanced (dbt desirable; ability to author, review and productionise SQL-based transformations and data pipelines).

• MLOps / Productionisation Intermediate to Advanced (CI/CD for models, containerisation, API endpoints, monitoring, drift detection and rollback strategies).

• Cloud & infra familiarity Familiar to Intermediate (AWS/GCP services relevant to analytics and model serving).

• Software engineering practices Familiar (version control, code reviews, testing, modular design and documentation).

Necessary skills, education and experience

Technical skills:

• Strong Python engineering and data science capabilities.

• Demonstrable experience delivering production models and analytics products.

• Advanced data visualisation and stakeholder-focused storytelling.

• Solid understanding of data warehouse design and ETL patterns.

• Experience implementing model governance and monitoring frameworks.

Business & interpersonal skills:

• Proven stakeholder management at senior levels; ability to influence priorities and drive cross-functional outcomes.

• Product-minded with experience scoping MVPs, prioritising features by impact, and tracking adoption.

• Coaching and people-management skills; experience growing and leading small analytics teams.

• Commercial awareness of insurance renewal dynamics and placement outcomes; capability to tie analytics to business KPIs.

Education:

• Required: Bachelors degree in a quantitative or analytical discipline (e.g., Statistics, Mathematics, Computer Science, Economics, Engineering) OR equivalent practical experience.

• Preferred: Masters degree (or equivalent) in a quantitative field; additional leadership or product management credentials desirable.

• Experience:

• Typical: 6+ years in analytics/data science roles with progressive responsibility and at least 12 years in a lead or senior role managing people and delivery.

• Desirable: Significant experience in insurance/financial services and in preparing models for production environments.

Marsh, a business of Marsh McLennan (NYSE: MMC), is the world's top insurance broker and risk advisor. Marsh McLennan is a global leader in risk, strategy and people, advising clients in 130 countries across four businesses: Marsh, Guy Carpenter, Mercer and Oliver Wyman. With annual revenue of $24 billion and more than 90,000 colleagues, Marsh McLennan helps build the confidence to thrive through the power of perspective. For more information, visit , or follow on LinkedIn and X.

Marsh McLennan is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age background, civil partnership status, disability, ethnic origin, family duties, gender orientation or expression, gender reassignment, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law. We are an equal opportunities employer. We are committed to providing reasonable adjustments in accordance with applicable law to any candidate with a disability to allow them to fully participate in the recruitment process. If you have a disability that may require reasonable adjustments, please contact us at

Marsh McLennan is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh McLennan colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one "anchor day" per week on which their full team will be together in person.

The applicable base salary range for this role is $130,000 to $259,800.

The base pay offered will be determined on factors such as experience, skills, training, location, certifications, education, and any applicable minimum wage requirements. Decisions will be determined on a case-by-case basis. In addition to the base salary, this position may be eligible for performance-based incentives.We are excited to offer a competitive total rewards package which includes health and welfare benefits, tuition assistance, 401K savings and other retirement programs as well as employee assistance programs.

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