DCX Product Analytics Analyst

The Product Analytics Analyst will partner with brokerage, client executives and product teams to deliver broker-facing insights that improve renewal placement outcomes for clients. You will run hands‑on analysis, build and validate models and prototype dashboards that inform broker decisions during renewals. You will translate broker workflows into analytics requirements, produce actionable deliverables, and work closely with engineers to operationalise models and measurement. Primary Responsibilities Engage with brokers and product stakeholders to capture renewal pain points and translate them into clear analytics problems and success criteria. Prepare, clean and feature-engineer data from existing systems and processes (policies, claims, quotes, broker notes, rating history) and third-party sources. Build, evaluate and validate models that produce required insights using appropriate techniques. Produce reproducible analysis notebooks and model artefacts (feature definitions, training pipelines, validation results). Prototype broker-facing dashboards and report mock-ups that translate model outputs into actionable broker guidance. Work with engineering/MLops to define data contracts, API specifications and acceptance criteria for operational models; support handover and testing. Document models, assumptions, data lineage and run bias / fairness checks in line with governance. Expected outputs and deliverables Models that provide insights to support the renewal discussion. Prototype dashboards and wireframes for broker workbench (MVP/iterative versions). Feature and data dictionaries, ETL specification notes and examples of SQL queries. Playbooks and short “how-to” guides for brokers to act on model-driven recommendations. Collaboration & stakeholder interactions (day-to-day) Brokers: run discovery sessions, pilot dashboards, gather feedback, iterate content and format; occasionally join broker renewal calls to observe workflows. Validate risk features, agree business rules and acceptability thresholds for automated recommendations. Engineering: write clear acceptance criteria, support user-acceptance testing, review deployment steps and monitor production behaviour. BI/UX: partner on dashboard design, data visualisations and ensuring insights are interpretable for non-technical users. Risk / Governance: provide documentation and respond to model governance queries; follow privacy and data access policies. Required tools, technologies and technical proficiencies (levels) Python — Intermediate to Advanced (pandas, scikit-learn, xgboost/lightgbm; testable scripting and notebooks).- Statistical modelling — Intermediate (classification/regression, feature engineering, cross-validation, calibration). BI & visualisation — Intermediate (Looker/Tableau/Power BI: prototype dashboards and deliverable-ready visualisations). Data warehousing — Familiar to Intermediate (Snowflake / BigQuery / Redshift; understand schemas, partitioning). ETL / transformation — Familiar (dbt desirable; ability to author and review SQL-based transformations). MLOps exposure — Familiar (experience packaging models, basic CI/CD, model monitoring concepts; not required to deploy end-to-end alone). Necessary skills, education and experience - Technical skills: Python scripting & data science libraries Data visualisation experience Core statistical understanding Familiarity with cloud data warehouses and ETL patterns. Exposure to MLOps concepts (versioning, monitoring) and Git. - Business & interpersonal skills: Strong stakeholder management and communication; able to translate technical results into actionable broker guidance. Product-minded: ability to scope MVPs and prioritise features for adoption. Commercial awareness of insurance renewal dynamics and placement outcomes. - Education: Required: Bachelor’s degree in a quantitative or analytical discipline (e.g., Statistics, Mathematics, Computer Science, Economics, Engineering) OR equivalent practical experience. Preferred: Master’s degree in a quantitative field. - Experience: Typical: 2–6 years in analytics/data science roles with demonstrable hands-on modelling experience. Desirable: 1–3 years’ exposure to insurance/financial services or broker workflows; experience 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 marsh.com, or follow on LinkedIn and X. Marsh McLennan is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, 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. 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.