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DoorDash's People Data Science team is looking to leverage AI and LLM-based models to extract insights from quantitative and qualitative employee data, aiming to inform talent strategy and improve organizational effectiveness.

Requirements

• 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).
• Experience creating data visualizations and dashboards using tools such as Sigma, Tableau, or Looker to communicate insights effectively.
• Experience with data science python packages such as PyTorch, TensorFlow, and Hugging Face Transformers.

Responsibilities

• 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.

Other

• 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).
• Ability to distill complex analyses into actionable insights through clear communication, visualization, and storytelling.
• 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.