Manager, People Analytics & Data Science

Mercury Insurance Services, LLC•Remote,

1d•$104,839 - $252,194•Remote

About The Position

Mercury is building a connected, trusted people analytics capability that puts decision-ready data and insights in leaders’ hands. The Manager, People Analytics & Data Science will help build that capability from the data foundation through the leader experience. This builder role combines technical depth and business judgment to make people data easier to trust, use, and apply in business decisions. The role builds reliable data pipelines and analytical datasets, establishes trusted metric definitions, and delivers dashboards that enable leaders to use people data independently. It also conducts targeted research and advanced analysis, including predictive and AI-enabled methods where appropriate, to surface insights about workforce risks, capacity, performance, productivity, and other priorities. The position turns business questions into data models and dashboards, and observed patterns into well-supported recommendations. The role partners with People and business leaders, Finance, HR Tech, Enterprise Data Science, Data & Analytics, data engineers, and other technical teams. It owns leader-facing people data products from discovery through launch and iteration, supports responsible and privacy-conscious analysis, and helps develop an analytics roadmap and standards for scalable, self-service people analytics. The position offers state-specific salary ranges, with a typical California hiring range of $171,148 to $209,181. The expected base salary varies based on relevant experience, skills, and location. An in-person interview may be required during the hiring process.

Requirements

Bachelor’s degree in data science, statistics, computer science, mathematics, engineering, economics, business analytics, or a related field.
At least 7 years of experience in data science, people analytics, analytics engineering, business intelligence, data engineering, or a related field.
Experience with People Analytics, HR data, workforce planning, talent, compensation, performance, or organizational research.
Demonstrated experience building data pipelines, analytical datasets, data models, or other reusable analytics infrastructure.
Demonstrated experience developing dashboards or self-service analytics products for business stakeholders.
Demonstrated experience applying statistical analysis, experimentation, forecasting, segmentation, or predictive modeling to complex business questions.
Demonstrated ability to communicate analytical findings clearly to senior leaders and non-technical audiences.
Strong hands-on skills with SQL and Python or R, plus experience with modern data pipelines, transformation, testing, and analytical workflows.
Experience with data modeling, semantic layers, BI tools, cloud data platforms, version control, and production-quality documentation.
Strong foundation in statistics, experimental design, causal reasoning, predictive modeling, and model evaluation.
Ability to distinguish reporting needs from diagnostic questions and research or modeling questions, and to select an appropriately rigorous solution.
Product-minded and user-centered approach, starting with the decision and designing the data experience around the user.
Strong storytelling skills, including making complex analysis clear, precise, and actionable without overstating certainty.
Collaborative, curious, pragmatic approach and comfort working across business and technical teams.
Strong judgment and discretion when handling confidential, sensitive, or personally identifiable People data.
Comfort using AI and collaboration tools to improve productivity while applying responsible-use principles.
Ability to build amid ambiguity, establish standards without over-engineering, and continuously improve work quality.

Responsibilities

Partner with data engineers to design, build, and maintain reliable pipelines and analytical datasets combining people, organizational, operational, and business data.
Develop reusable data models, transformations, validation checks, documentation, and monitoring to support accurate, repeatable analysis.
Partner with HR Tech, Enterprise Data Science, Data & Analytics, Finance, and other technical teams on source systems, architecture, integrations, access, and data quality.
Translate business and People questions into data requirements, including grains, dimensions, logic, refresh expectations, and ownership.
Establish and maintain metric contracts documenting definitions, calculation logic, sources, refresh cadence, interpretation, stewardship, and action paths.
Improve the reliability, transparency, privacy, and usability of people data as Mercury evolves its HRIS and broader data ecosystem.
Own the end-to-end delivery of leader-facing people data products, from discovery and prioritization through launch, adoption, and iteration.
Build operational and executive dashboards that connect people metrics to business and workforce outcomes.
Create focused dashboard experiences that help leaders understand what changed, possible contributing factors, and potential next actions.
Design self-service experiences through intuitive personas, access patterns, definitions, documentation, enablement, and feedback loops.
Embed relevant people insights in existing executive reporting and operating rhythms where possible.
Measure product usage, trust, decision relevance, and adoption; use feedback and evidence to expand, refine, or retire products.
Conduct targeted research and analysis on questions including 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 to the question and data quality.
Translate complex analysis into clear findings, limitations, implications, and recommendations for People and business leaders.
Design analyses with appropriate validation, explainability, privacy protections, and bias awareness; use models as decision support rather than a substitute for accountable human judgment.
Develop repeatable approaches for research intake, hypothesis formation, analysis, peer review, storytelling, and follow-through.
Partner with stakeholders to connect analytical findings to decisions, interventions, and impact measures.
Build trusted relationships with People leaders, business leaders, Finance, HR Tech, Enterprise Data Science, and other partners.
Shape and maintain an analytics roadmap balancing foundational data work, self-service products, and targeted research.
Establish practical standards for data quality, versioning, testing, documentation, access, and responsible use of people data.
Help leaders and People partners build confidence and fluency in using people metrics and insights.
Lead through influence and contribute to a high-performing, collaborative People Ops, Tech & Analytics team.

Benefits

Competitive compensation
Flexibility to work from anywhere in the United States for most positions
Paid time off, including vacation time, sick time, 9 paid Company holidays, and volunteer hours
Incentive bonus programs, potentially including holiday, referral, and performance-based bonuses
Medical insurance
Dental insurance
Vision insurance
Life insurance
Pet insurance
401(k) retirement savings plan with company match
Engaging work environment
Promotional opportunities
Education assistance
Professional and personal development opportunities
Company recognition program
Health and wellbeing resources, including free mental wellbeing therapy/coaching sessions and child and eldercare resources