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 experiments and models
• Troubleshoot model performance issues, data anomalies, and code bugs independently
• Define analytical approaches and scope projects for moderately complex or ambiguous business problems
• Partner with product managers and stakeholders to define success metrics and translate problems into data science solutions
• Lead design and analysis of experiments (A/B tests, switchback) and interpret results for actionable insights
• Identify opportunities for data science value and initiate exploration
• Follow and improve team processes for coding standards, documentation, reproducibility, and experimentation
• Mentor junior data scientists (DS I and DS II), review code and analyses, support skill growth
• Influence technical decisions on modeling, validation, and tooling
• Drive improvements to team standards and best practices; own specific team practices or components
• Educate stakeholders on data science capabilities and limitations, explain complex methodologies clearly
• Participate in recruiting and provide high-quality 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 Ph.D. in related field with 2+ years of relevant experience
• Ability to independently own full data science lifecycle from problem formulation to model deployment and maintenance
• Expertise in machine learning and statistics (gradient-boosted models, deep neural networks, time series, causal inference, experimentation design)
• Strong foundation in probability and statistics for large datasets
• Experience designing and analyzing experiments (A/B testing) and building 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 for production deployment
• Strong communication skills for technical and non-technical audiences

Tech stack

Benefits