Data Scientist III

What you'll do

• Own end-to-end data science lifecycle for medium-to-large projects including data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining
• Apply expertise in machine learning and statistics to select appropriate methods for complex problems
• Write efficient, modular, well-tested code leveraging distributed tooling like Vertex AI pipelines, Dataflow, BigQuery
• Design and implement robust validation frameworks for complex experiments and models
• Troubleshoot model performance issues, data anomalies, and code bugs with little guidance
• 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
• Lead design and analysis of experiments (A/B tests, switchback) and interpret results for actionable insights
• Identify opportunities where data science can add value and initiate exploration
• Follow and improve team processes for coding standards, documentation, reproducibility, and experimentation
• Mentor junior data scientists, review code and analyses, support their skill growth
• Influence technical decisions on modeling, validation, and tooling
• Drive improvements to team standards and data science best practices
• Educate stakeholders on capabilities and limitations of data science models
• Participate in recruiting and provide interview feedback

Requirements

• B.S. or M.S. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or related technical field with 5+ years of relevant industry experience; OR a Ph.D. in a related field with 2+ years of relevant experience
• Ability to independently own the full data science lifecycle from problem formulation and feature engineering through model deployment, monitoring, and ongoing maintenance
• Expertise in core areas of machine learning and/or statistics such as gradient-boosted models, deep neural networks, time series, causal inference, experimentation design
• Strong foundation in probability and statistics including techniques that scale to large datasets
• Experience designing and analyzing experiments (e.g., A/B testing) and building robust model and experiment validation frameworks
• Strong Python and SQL skills
• Experience with ML frameworks such as TensorFlow or PyTorch
• Ability to write efficient, modular, well-tested code and collaborate with engineering to move models and analyses into production
• Strong communication skills to convey complex technical concepts to technical and non-technical audiences

Tech stack

Benefits