Manager, People Analytics & Data Science
Mercury Insurance · Remote, UNAVAILABLE, US
About this role
## Manager, People Analytics & Data Science ### Overview Mercury is building a connected, trusted people analytics capability that delivers decision-ready data and insights to leaders—when and where they need them. This is a builder role focused on creating reliable data pipelines, trusted metric definitions, and robust dashboards for self-service people data. You’ll also conduct targeted research and advanced analysis (including predictive and AI-enabled methods where appropriate) to surface insights on workforce risks, capacity, performance, productivity, and other priority areas. ### Responsibilities - **Data foundation & analytics engineering** - Partner with data engineers to design, build, and maintain reliable data pipelines and analytical datasets. - Develop reusable data models, transformations, validation checks, documentation, and monitoring. - Collaborate with HR Tech, Enterprise Data Science, Data & Analytics, Finance, and other teams on source systems, architecture, integrations, access, and data quality. - Translate business/People questions into clear data requirements (grains, dimensions, logic, refresh expectations, ownership). - Establish and maintain **metric contracts** (definitions, logic, sources, refresh cadence, interpretation, stewardship, and action path). - **Dashboards & self-service people data** - Own end-to-end delivery of leader-facing people data products (discovery → launch → adoption → iteration). - Build operational and executive dashboards connecting people metrics to business/workforce outcomes. - Create focused experiences that help leaders answer: **what changed, what may be contributing, and what action to take next**. - Design for self-service (personas, access patterns, definitions, documentation, enablement, feedback loops). - Embed insights into existing reporting/operating rhythms where possible. - Measure usage, trust, decision relevance, and adoption; expand/refine/retire products based on evidence. - **Research, advanced analytics, and predictive insight** - Conduct research on prioritized questions such as attrition risk, critical-role readiness, capacity constraints, workforce planning, manager effectiveness, performance, productivity, and AI-enabled work. - Apply exploratory analysis, statistical methods, experimentation, forecasting, segmentation, and predictive modeling as appropriate. - Communicate findings clearly, including limitations, implications, and recommendations. - Ensure validation, explainability, privacy protections, and bias-awareness; treat models as decision support. - Build repeatable research intake → hypothesis → analysis → peer review → storytelling → follow-through. - Partner with stakeholders to connect insights to decisions and measures of impact. - **Partnership & capability building** - Build trusted relationships across People leaders, business leaders, Finance, HR Tech, Enterprise Data Science, and others. - Shape and maintain an analytics roadmap balancing foundational work, self-service products, and targeted research. - Set standards for data quality, versioning, testing, documentation, access, and responsible use. - Help leaders and People partners build confidence and fluency in using people metrics. - Lead through influence and contribute to a high-performing, collaborative People Ops, Tech & Analytics team. ### What success looks like - Leaders can access **trusted** people metrics in the tools and
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