People Analytics Staff Data Scientist

The role

The People Analytics team is moving beyond descriptive dashboards to deliver predictive and advanced analytical capabilities that drive talent and business outcomes. As a Staff People Data Scientist, you will design, build, and productionize models and analyses (e.g. attrition risk, headcount forecasts, mobility, quality of hire and etc) that inform decisions across the employee lifecycle. You’ll be a hands‑on builder and thought partner to People Analysts, COEs (e.g., Talent Management, Total Rewards, TA), and cross‑functional stakeholders in Legal/Privacy, Security, Finance, Product, and Engineering.

Bonus: Experience with AI (e.g., LLMs, NLP) is a plus and may be used to bring analyses to life via simple apps or assistants when it meaningfully improves access and adoption.

How This Role Elevates the People Team (and Company)

This role unlocks deeper, predictive insights into employee behavior and program effectiveness, enabling better talent decisions, more equitable processes, and measurable business impact (e.g., reduced turnover cost, improved hiring efficiency, better workforce allocation). Where appropriate, we will scale insights through lightweight, user‑friendly tools (potentially AI‑assisted) that meet users where they are.

What you’ll do:

Build predictive capabilities across the employee lifecycle

• Develop, validate, and productionize models for attrition risk, internal mobility, recruiting funnel yield, quality of hire, performance and success trajectories, engagement drivers, and workforce/headcount forecasting.
• Stand up evaluation, calibration, monitoring, and drift detection; own the model lifecycle from design through deployment and iteration.

Apply advanced analytical methods to business questions

• Design and implement segmentation, decision trees, survival/time‑to‑event, time series, hypothesis testing, uplift modeling, and anomaly detection to surface drivers and shape recommendations.
• When useful, apply NLP to survey and case text to connect attitudinal and behavioral data.
• Translate complex analyses into clear narratives, visuals, and recommendations for executives and frontline stakeholders; create enablement that moves decisions and outcomes.

Advance the function

• Partner with Analysts/COEs to design A/B and quasi‑experimental evaluations of People programs (e.g., onboarding, recognition, manager training) and translate results into clear actions.
• Serve as a technical subject‑matter expert, mentoring analysts on statistical methods, ML best practices, experimentation, and code quality.
• Bonus (optional): Package insights as internal tools (e.g., simple APIs, Streamlit apps, Tableau extensions) including AI‑assisted features where they add clear value.

Data foundations and operations

• Partner with People Data Engineering to define features and governed datasets in Snowflake

• Navigate the compliance and regulatory requirements for proper model and AI use across the People Team.
• Embed privacy‑by‑design and fairness checks (bias detection/mitigation, explain ability); align with Legal/Privacy and Security on data governance and appropriate use.

What you’ll need:

Education: Bachelor’s in Computer Science, Statistics, Economics, Engineering, Data Science, or a quantitative field (Master’s preferred).

Experience: 7+ years in applied data science/ML (ideally in People Analytics, Talent, or adjacent domains) with a track record of shipping models/analyses that change decisions.

Technical depth:

• Strong ability to leverage a dimensional data model in Snowflake to build datasets for advanced analytics.
• Python (pandas, scikit‑learn, statsmodels), SQL, and Tableau for analytics & visualization or similar tools..
• Deep expertise in statistics and data science methods (e.g., linear/logistic regression, causal inference, A/B testing, matching methods, survival/time series).

Business influence: Ability to scope ambiguous problems, balance speed and rigor, and communicate clearly with technical and non‑technical partners; strong, proactive ownership.

Nice to have:

• People‑data fluency: Experience with HRIS/ATS/survey sources (e.g., Workday, Greenhouse, Qualtrics/Glint), text analytics on engagement/case data, and KPI design (e.g., quality of hire, time‑to‑fill, internal mobility).
• Background in I/O Psychology or psychometrics; experience with pay equity, fairness metrics, and/or differential privacy.
• Building internal tools with Streamlit or lightweight web frameworks; AWS data/ML services.
• Experience mapping people outcomes to financial impact (productivity, efficiency, turnover cost).
• Experience with Snowflake, dbt, Airflow, Git, and model monitoring in production.
• AI/LLM experience (prompting, RAG, safety guardrails) to enhance discoverability and access to insights.