Data Scientist III

Key Responsibilities

• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.
• Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex problems.
• Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate.
• Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance.
• Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance.

Execution & Collaboration

• Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems.
• Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions.
• Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results with a focus on actionable insights and business outcomes.
• Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration.
• Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation.

Mentorship & Influence

• Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models.
• Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling.
• Drive improvements to team standards, data science best practices, and analytical rigor.
• Educate stakeholders on the capabilities and limitations of data science models, and explain complex methodologies to both technical and non-technical audiences.
• Participate actively in recruiting, providing high-quality interview feedback for candidates up to this level.