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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager, People Analytics & Data Science - **Company:** Mercury - **Location:** New York, NY, United States (Remote available) - **Experience:** Starter - **Salary:** $105,000.0 - $252,000.0 - **Contract:** Permanent contract - **Skills:** Microsoft Access, Agile Methodology, Artificial Intelligence, Data Analysis, Cloud Computing, Collaborative Software, Information Engineering, Data Infrastructure, Decision Support Systems, DevOps, Human Resources Information System (HRIS), HR Analytics, Python (Programming Language), Cloud Services, Software Systems, SQL Databases, Management of Software Versions, Enterprise Data Management, Model Validation, Data Layers, Information Technology, Software Version Control, Data Pipelines - **Published:** October 16, 2026 - **Apply:** https://www.builtincolorado.com/job/manager-people-analytics-data-science/11733024?handler=ApplyRedirect ## About the Role * Bachelor's degree in data science, statistics, computer science, mathematics, engineering, economics, business analytics, or a related field, * 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. Knowledge and Skills: * Able to distinguish between a reporting need, a diagnostic question, and a research or modeling question-and choose an appropriately rigorous solution. * Product-minded and user-centered: starts with the decision and designs the data experience around the user. * Strong storyteller who can make complex analysis clear, precise, and actionable without overstating certainty. * Highly collaborative, curious, pragmatic, and comfortable working across business and technical teams. * Strong judgment and discretion when handling confidential, sensitive, or personally identifiable People data. * Comfortable using AI and collaboration tools to improve productivity while applying responsible-use principles. * Able to build in ambiguity, establish standards without over-engineering, and continuously improve the quality of the work., Field-based sales role responsible for expanding SOPHiA GENETICS' genomic solutions across Washington, Oregon, and Northern California. Develop new business, build relationships with oncologists, pathology teams, laboratories, and senior decision-makers, explain genomic testing value, address clinical and technical questions with subject-matter experts, and convert opportunities into bookings. Candidates need oncology or molecular diagnostics sales experience and knowledge of genomic biomarkers and treatment decision-making., Leads the design and development of software solutions, resolves complex technical problems, and provides guidance to engineering colleagues. Responsibilities include preparing technical documentation, reviewing code and testing, applying modern development methodologies, and supporting business initiatives. The role typically requires a bachelor's degree and at least three years of relevant experience. Preferred skills include Agile, cloud computing, DevOps, requirements analysis, system design, and project management. ## Description The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location. ResponsibilitiesData foundation and analytics engineering * Partner with data engineers to design, build, and maintain reliable data pipelines and analytical datasets that bring together people, organizational, operational, and business data from relevant sources. * Develop reusable data models, transformations, validation checks, documentation, and monitoring that support accurate and repeatable analysis. * Partner with HR Tech, Enterprise Data Science, Data & Analytics, Finance, and other technical teams on source systems, data architecture, integrations, access, and data quality. * Translate business and People questions into clear data requirements, grains, dimensions, logic, refresh expectations, and ownership. * Establish and maintain metric contracts that document definitions, calculation logic, sources, refresh cadence, interpretation, stewardship, and action path. * Improve the reliability, transparency, privacy, and usability of people data as Mercury evolves its HRIS and broader data ecosystem. Dashboards and self-service people data * Own the end-to-end delivery of leader-facing people data products, from discovery and prioritization through launch, adoption, and iteration. * Build robust operational and executive dashboards that connect people metrics to business and workforce outcomes. * Deliver focused dashboard experiences that help leaders answer what changed, what may be contributing, and what action to take next. * Design for self-service by developing intuitive personas, access patterns, definitions, documentation, enablement, and feedback loops. * Embed relevant people insights into existing executive reporting and operating rhythms wherever possible rather than creating disconnected destinations. * Measure usage, trust, decision relevance, and adoption; use feedback and evidence to expand, refine, or retire products. Research, advanced analytics, and predictive insight * Conduct targeted research and analysis 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 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; treat models as decision support, not 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 measures of impact. Partnership and capability building * Build trusted relationships with People leaders, business leaders, Finance, HR Tech, Enterprise Data Science, and other partners. * Shape and maintain an analytics roadmap that balances 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. What success looks like * Leaders can access trusted people metrics in the tools and business rhythms where decisions already happen. * Core dashboards are clear, adopted, well-governed, and connected to meaningful business questions. * Data pipelines, metric definitions, and analytical products are reliable enough to support recurring decisions-not just one-time requests. * Targeted research produces new, decision-relevant insight on priority workforce questions. * The team has a scalable roadmap for expanding self-service, predictive analysis, and future HRIS-enabled analytics.