Data Scientist, People

Replit1 months ago

Location

Foster City, CA

Type

Full Time

Salary

USD 210,000 – 350,000

Level

Senior

Role

Data Scientist

Posted

May 19, 2026

Full TimeSenior

The role

Summary

As a Data Scientist, People at Replit, you'll architect and deploy AI-driven analytics systems that transform talent decision-making across hiring, compensation, performance management, and organizational design. This senior-level role combines advanced statistical modeling, LLM-powered automation, and people analytics expertise to build intelligent systems that move Replit from traditional reporting to real-time, AI-native workforce intelligence. You'll partner with HR, Recruiting, Finance, and Data Engineering to develop predictive models, agentic workflows, and analytical frameworks that help leaders make faster, data-informed talent decisions at scale.

What you'll do

Compensation Analytics Systems: Build and maintain live compensation analytics systems that integrate Ashby offer data, band positioning, acceptance rates, and market benchmarks. Create automated models that recommend specific compensation adjustments while ensuring internal equity and external market competitiveness. Develop dashboards and alerts that flag compensation anomalies and track competitive positioning across talent markets.

Predictive Modeling for People Decisions: Develop and deploy predictive models that enable faster, data-driven talent decisions. Build regretted attrition models that identify at-risk employees 90+ days in advance with actionable signals, promotion prediction models, and hiring success indicators. Surface model outputs directly into manager workflows and recruiting systems to reduce decision-making time and improve outcomes.

AI Agent Development and Deployment: Design and deploy agentic AI workflows that generate first-pass recommendations for high-stakes people decisions including compensation adjustments, promotions, and hiring selections. Implement LLM-powered systems that enable people leaders to review and refine recommendations rather than starting from scratch, reducing analysis time while maintaining human oversight and accountability.

Recruiting Analytics and ROI Optimization: Build comprehensive recruiting analytics layers connecting sourcing channels to time-to-hire, first-year performance, and tenure outcomes. Analyze recruiting spend efficiency, identify high-performing channels, and surface weekly insights to recruiting leadership. Provide data-driven recommendations for reallocating recruiting resources to maximize hiring velocity and quality.

Organizational Design Analysis: Analyze organizational structure effectiveness across dimensions including spans and layers, talent density, hiring efficiency, and role progression. Identify organizational inefficiencies such as over-leveling, under-leveling, and structural constraints. Provide insights to support organizational redesign decisions and support executive-level discussions on optimal org structure.

Workforce Planning and Financial Modeling: Partner with Finance to transition from manual spreadsheet-based workforce planning to live, automated models. Build systems that account for attrition patterns, hiring velocity, and ramp time by function. Create forecasts that enable proactive headcount planning and budget allocation across departments.

Unstructured Data Analysis at Scale: Apply NLP and LLM technologies to analyze unstructured people data including support tickets, exit interviews, performance reviews, and engagement survey responses. Extract patterns and insights from qualitative data at scale, transforming subjective feedback into quantitative signals that inform people strategy and organizational improvements.

Real-Time Insights and Automated Reporting: Replace traditional quarterly reporting cycles with always-on agentic systems that surface relevant insights to leaders when needed. Build dashboards and alert systems that proactively notify stakeholders of important people metrics, anomalies, and opportunities without requiring manual report generation or stakeholder requests.

Strategic People Analytics Support: Support high-stakes organizational and talent decisions with rigorous statistical analysis and causal inference. Analyze executive hiring outcomes, retention drivers, reorganization impacts, and other strategic people initiatives. Communicate complex analytical findings clearly to executive leadership and cross-functional stakeholders.

What we look for

Technical

SQL and PythonDemonstrated mastery of SQL for complex data querying, transformation, and analysis across large-scale datasets. Advanced Python proficiency for statistical computing, data manipulation, and building analytical frameworks using libraries such as Pandas, NumPy, Scikit-learn, and StatsModels.

Predictive Modeling and Machine LearningStrong background building and deploying predictive models for business decision-making. Experience with classification, regression, clustering, and time-series forecasting. Understanding of model evaluation metrics, cross-validation, feature engineering, and techniques to prevent overfitting in production systems.

Statistical Methods and ExperimentationStrong statistical foundation including hypothesis testing, statistical inference, and experimental design. Experience designing and analyzing A/B tests, quasi-experimental studies, and observational analyses. Proficiency with causal inference methodologies to establish cause-and-effect relationships in complex systems.

Large-Scale Data AnalysisProven experience working with large-scale operational and behavioral datasets including HR systems, recruitment platforms, and payroll data. Ability to design schemas, optimize queries, and manage data pipelines that process and analyze millions of records efficiently.

Large Language Models and AI IntegrationDemonstrated experience leveraging LLMs and generative AI in analytics workflows. Experience with prompt engineering, fine-tuning approaches, building agentic systems, and integrating LLM outputs into production analytics platforms. Understanding of LLM limitations, hallucination mitigation, and responsible AI practices.

Data Visualization and CommunicationAbility to translate complex analytical findings into clear, compelling visualizations and narratives for executive and non-technical audiences. Proficiency with visualization tools such as Tableau, Looker, or similar platforms. Experience creating dashboards that drive decision-making and communicate insights effectively.

Education

Bachelor's Degree in Quantitative FieldBachelor's degree in Computer Science, Statistics, Mathematics, Economics, or related quantitative field. Equivalent practical experience with demonstrated expertise in statistical methods and data analysis may be considered.

Experience

People Analytics BackgroundMinimum 6 years total experience with at least 3+ years in people analytics, compensation analytics, workforce analytics, or related human capital analytics roles. Deep familiarity with talent acquisition metrics, employee retention analysis, compensation equity analysis, and organizational analytics.

Data Science and Analytical ModelingSenior-level or equivalent data science experience building analytical frameworks and predictive models that drive business decisions. Track record of moving beyond descriptive analytics to develop prescriptive and predictive systems that improve organizational outcomes and decision velocity.

Cross-Functional Leadership and InfluenceDemonstrated ability to partner effectively with HR, Finance, Recruiting, and Data Engineering teams. Experience translating business problems into analytical approaches and communicating findings to executives, managers, and technical teams. Proven influence across organizational boundaries.

High-Growth or AI-Native EnvironmentExperience operating in fast-moving, high-growth environments such as venture-backed startups or AI-native companies. Comfort with ambiguity, changing priorities, and building systems in conditions of incomplete information. Prior exposure to scaling analytical systems as company grows from hundreds to thousands of employees.

Skills

Required skills

SQLAdvanced SQL for querying, aggregating, and transforming large datasets in production analytics systems.

PythonAdvanced Python including data manipulation (Pandas), numerical computing (NumPy), machine learning (Scikit-learn), and statistical analysis.

Predictive ModelingBuilding classification, regression, and forecasting models for operational decision-making with rigorous evaluation methodologies.

Statistical Analysis and Causal InferenceStatistical hypothesis testing, experimental design, A/B testing, and causal inference techniques to establish cause-and-effect relationships.

People Analytics Domain KnowledgeDeep understanding of compensation structures, talent acquisition funnel, retention drivers, organizational design, and workforce planning concepts.

Large Language Models and Generative AIPractical experience using LLMs in analytical workflows, including prompt engineering, agentic system design, and integrating AI outputs into production systems.

Executive CommunicationAbility to communicate complex analytical findings, statistical concepts, and recommendations to executive leaders and non-technical stakeholders in clear, actionable language.

Nice to have

Modern Data Stack ToolsFamiliarity with modern cloud data platforms and transformation tools such as dbt, BigQuery, Snowflake, Redshift, or Databricks. Experience building and maintaining data pipelines and data models.

People Systems ExpertiseExperience with HR Information Systems (HRIS), talent management platforms, and compensation tools such as Ashby, Rippling, Lattice, Carta, Workday, or SuccessFactors.

Natural Language ProcessingExperience with NLP techniques, text analysis, sentiment analysis, and transforming unstructured text into quantitative features for analytical modeling.

Internal Tools and AutomationTrack record of building internal tools, dashboards, automated workflows, or agentic systems that improve organizational efficiency and decision-making velocity.

AI-Native Company ExperienceBackground building products or infrastructure at companies built natively on AI principles, understanding how AI shapes product, operations, and organizational strategy.

Replit Platform FamiliarityPrior experience building applications or projects on the Replit platform, demonstrating familiarity with browser-based development and Replit's developer experience.