Data Scientist, People

DoorDash USA-posted 8 days ago
Full-time • Mid Level
Los Angeles, NY
5,001-10,000 employees
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The People Data Science team is part of DoorDash’s People Intelligence organization, a multidisciplinary group that combines data science, business intelligence, analytics engineering, and data engineering to power our people decisions with trusted, scalable insights. Together, we enable DoorDash to unlock the full potential of our talent and drive measurable improvements in organizational effectiveness. As the advanced analytics arm of People Intelligence, the People Data Science team applies statistical modeling, machine learning, and AI-driven analysis to uncover trends and patterns across the employee lifecycle — from hiring to engagement to retention. By transforming people data into meaningful insights, we help DoorDash foster a thriving, high-performing workforce and make data-informed decisions that elevate the employee experience and organizational impact. About the Role As a Data Scientist on the People Team at DoorDash, you will shape the future of people analytics by building and deploying AI and LLM-based models that deliver insights from both quantitative and qualitative employee data. You’ll design intelligent systems and agents that leverage large-scale employee experience, performance, and organizational data — integrating structured and unstructured signals to uncover trends that inform talent strategy and business outcomes. This role blends deep expertise in statistical modeling, natural language processing, and applied machine learning with a strong understanding of people analytics and business context. You’ll collaborate with data engineers, applied scientists, and People Business Partners to develop scalable insight-generation systems that help leaders make more informed, data-backed decisions. You're excited about this opportunity because you will... Build AI-powered people analytics tools — develop LLM- or agent-based systems that summarize employee sentiment, extract insights from qualitative feedback, and surface trends in quantitative data. Apply advanced statistical and ML techniques to understand drivers of engagement, retention, and performance. Design, test, and deploy scalable models and pipelines that analyze large volumes of survey, feedback, and HR data. Collaborate cross-functionally with People, Engineering, and Product partners to design AI solutions that support business strategy and improve the employee experience. Translate data into action — tell compelling stories with data that shape leadership decisions and organizational priorities. Explore cutting-edge GenAI and NLP approaches — from embeddings and topic modeling to fine-tuning LLMs for people analytics applications. Contribute to the evolution of People Data Science at DoorDash — shaping our approach to scalable, AI-driven insights for the future of work.

  • Build AI-powered people analytics tools — develop LLM- or agent-based systems that summarize employee sentiment, extract insights from qualitative feedback, and surface trends in quantitative data.
  • Apply advanced statistical and ML techniques to understand drivers of engagement, retention, and performance.
  • Design, test, and deploy scalable models and pipelines that analyze large volumes of survey, feedback, and HR data.
  • Collaborate cross-functionally with People, Engineering, and Product partners to design AI solutions that support business strategy and improve the employee experience.
  • Translate data into action — tell compelling stories with data that shape leadership decisions and organizational priorities.
  • Explore cutting-edge GenAI and NLP approaches — from embeddings and topic modeling to fine-tuning LLMs for people analytics applications.
  • Contribute to the evolution of People Data Science at DoorDash — shaping our approach to scalable, AI-driven insights for the future of work.
  • Master’s or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics, Economics, Industrial-Organizational Psychology (quantitative track), or a related field.
  • 3+ years of experience applying data science methods to real-world problems (1–2+ years in People Analytics preferred).
  • Proficiency in Python and SQL, with experience using ML and NLP libraries (e.g., scikit-learn, statsmodels).
  • Proven experience building or applying large language models (LLMs) and NLP-based systems for text summarization, sentiment analysis, or insight extraction.
  • Strong foundation in statistical modeling, causal inference, and experimental design (e.g., regression, clustering, A/B testing, time-series).
  • Experience designing and scaling data pipelines using Snowflake, dbt, Databricks.
  • Familiarity with LLM orchestration tools (e.g., LangChain, LlamaIndex, or similar frameworks) and vector databases (e.g., Postgres with pgvector).
  • Ability to distill complex analyses into actionable insights through clear communication, visualization, and storytelling.
  • Experience creating data visualizations and dashboards using tools such as Sigma, Tableau, or Looker to communicate insights effectively.
  • Passion for building AI solutions that empower people leaders and improve organizational decision-making through ethical and responsible applications of data science.
  • Must be comfortable regularly exercising discretion and independent judgment in performing job duties, including evaluating options, making informed decisions, and determining appropriate courses of action within the scope of assigned responsibilities.
  • Experience building or contributing to AI analytics assistants or chatbots that enable natural language access to insights.
  • Experience with data science python packages such as PyTorch, TensorFlow, and Hugging Face Transformers.
  • Experience in survey analytics, employee engagement research, or text analytics using large-scale feedback data.
  • Familiarity with HRIS systems (e.g., Workday) and core workforce metrics such as attrition and engagement.
  • Exposure to fine-tuning foundation models and evaluating LLM performance for reliability and bias.
  • Experience with graph databases (e.g., Neo4j) for modeling organizational networks.
  • Background in applied behavioral science, organizational research, or people analytics experimentation.
  • a 401(k) plan with employer matching
  • 16 weeks of paid parental leave
  • wellness benefits
  • commuter benefits match
  • paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act)
  • medical, dental, and vision benefits
  • 11 paid holidays
  • disability and basic life insurance
  • family-forming assistance
  • a mental health program
  • flexible paid time off/vacation, plus 80 hours of paid sick time per year
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