- Own the end-to-end data science lifecycle for moderately complex models spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.
- Apply expertise in machine learning and statistics including gradient-boosted models, deep neural networks, time series, causal inference, and experimentation design.
- Write efficient, modular, well-tested code for data processing and model training/inference.
- Define analytical approaches and scope data science projects for ambiguous business problems.
- Partner with product managers and stakeholders to translate marketplace problems into data science solutions.
- Lead the design and analysis of experiments and interpret complex model results.
- Mentor junior scientists by providing technical guidance and reviewing code, analyses, and models.
PythonSQLMachine Learning+3 more