Senior People Data Scientist

Overview

Instacart’s People Analytics & Research (PAR) team delivers trusted insights that help leaders make better, faster decisions and create an environment where every employee can do the best work of their career. Embedded within People Services Delivery, we partner across the People org and the business to build the data infrastructure, dashboards, analytical frameworks and research that power workforce planning, organizational health, and strategic HR initiatives for all Instacart employees. In addition, our mission is to bring these insights to life through close partnerships with partner teams across Instacart.

We’re hiring a Senior People Data Scientist (L5) to serve as an enterprise‑wide people analytics thought partner—blending data engineering, analytics, consulting, and data product ownership. You will own complex, multi‑quarter analytical workstreams, shape company‑level people metrics, and help leaders at all levels use data to make better, more equitable decisions about our workforce.

This is a high‑visibility IC role with significant exposure to senior HR and business leaders, ideal for someone who is equally comfortable in Snowflake, extracting insights from employee data, executive‑level storytelling, and in shaping how HR and business leaders across Instacart use data at scale.

About the Job

• Help to evolve the enterprise people analytics agenda across key domains (e.g., organizational health, performance, hiring), including cross functional partnerships to align metrics to Instacart’s priorities and providing insights which help solve our more complex people problems.
• Contribute to high‑stakes, enterprise‑wide projects, such as:
• Drivers of retention across functions
• Implementation and analysis of people surveys across Instacart
• Organizational Health and design metrics
• Engagement survey insights and action effectiveness
• Implementation of AI in analysis workflows
• Design and mature self‑serve people data products (dashboards, standardized views, metric layers) that scale across HR and the business—standardizing definitions, partnering with People Tech and Finance on data architecture, and driving adoption through enablement and training.
• Own Data Warehousing and Data Architecture design decisions spanning across data ingestion, ETL/ELTs through BI Tools and LLMs.
• Bring analytical rigor to enterprise People programs (e.g., performance cycles, comp reviews, workforce planning, AES) by defining success metrics, segmenting impact, and recommending changes based on evidence and HRBP‑style judgment about feasibility and change management.
• Apply and interpret advanced methods where needed, such as predictive attrition models, cohort/survival analysis, and simple causal frameworks to evaluate program effectiveness, while keeping methods transparent and explainable to HR and business audiences.
• Serve as a partner to HRBPs, People leaders, and analysts on data literacy, metric interpretation, and responsible use of HR data. Contribute meaningfully to the team’s move from ad hoc requests to thought partnership and guidance on the appropriate use of people data for decision making.
• Champion data governance, privacy, and role‑based access across Workday, Snowflake, BI tools, and Qualtrics, partnering with People Tech and vendors to ensure HR data is accurate, secure, and fit for sensitive people decisions.
• Contribute to PAR’s roadmap, operating model, and culture—refining intake and prioritization, setting bar‑raising standards for analysis and storytelling.

About You

Minimum Qualifications

• 5+ years of experience in analytics, data science, business intelligence, or a closely related field, with at least 2–3+ years in People Analytics / HR data (HCM, recruiting, comp, retention, DEI, engagement, or workforce planning).
• Experience designing and maintaining executive‑facing dashboards and data products in a BI tool (e.g., OneModel, Tableau, Looker), including metric design, documentation, and stakeholder enablement/training.
• Advanced proficiency in SQL and experience with a modern cloud data warehouse such as Snowflake, including working with large, complex datasets, performance‑tuned queries, building data pipelines, and developing analytics-ready models.
• Demonstrated ability to translate complex analytical findings into clear stories and actionable insights for HR/business leaders, including writing talking points and delivering live presentations.
• Demonstrated expertise in multivariate analysis, including the design and interpretation of regression models, comparison testing, and natural language processing techniques applied to large-scale text data.
• Strong foundation in research methodology and psychometrics, with the ability to design, validate, and interpret survey instruments and other qualitative measures with rigor.
• Experience handling sensitive HR data with strong judgment around privacy, security, and role‑based access controls.
• Bachelor’s degree in a quantitative or relevant field (e.g., Statistics, Economics, Data Science, Industrial/Organizational Psychology, Computer Science) or equivalent practical experience.

Preferred Qualifications

• Experience in People Analytics or Workforce Analytics teams that moved a function from ad‑hoc reporting to scalable, self‑serve analytics (e.g., standard dashboards, templates, data education programs).
• Depth with Workday, Greenhouse, OneModel, Qualtrics, or similar tools, including how data flows between systems and how to design analytics‑ready data structures.
• Experience designing and analyzing employee survey programs (engagement, lifecycle surveys, program evaluation, pulsing), including segmentation and action‑planning support for HRBPs and leaders.
• Background operating in a fast‑paced, high‑growth, or public‑company environment with multiple senior stakeholders and competing priorities.
• Demonstrated influence in cross‑functional settings, partnering with HRBPs, Total Rewards, Recruiting, Finance, and People Tech to land complex initiatives with both technical and change‑management components.

Masters degree in a quantitative or relevant field (e.g., Statistics, Economics, Data Science, Industrial/Organizational Psychology, Computer Science) or equivalent practical experience.

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