Data Scientist
Skills
- Strong statistical reasoning: experiment design, causal inference, signal vs noise distinction.
- Proven end-to-end machine learning ownership and production experience.
- Proficiency in Python and SQL; experience with production data pipelines and tooling (Spark, Airflow, Ray, SageMaker, Vertex, etc.).
- Experience translating complex analyses into clear business recommendations.
- Comfort solving ill-defined problems and defining success metrics and tradeoffs.
- High data-quality standards, including validation and bias analysis.
- Excellent communication and storytelling for diverse audiences.
Responsibilities
- Build and maintain pricing metrics, dashboards, and analytical frameworks to track model and business performance.
- Design, run, and analyze experiments and causal studies to measure impact and guide pricing decisions.
- Develop predictive and statistical models to improve pricing accuracy and reduce risk.
- Own end-to-end ML lifecycle: data acquisition, feature engineering, model development, validation, deployment, and monitoring.
- Collaborate with Product, Engineering, and Operations to prioritize roadmap items and operationalize models.
- Communicate findings and narratives to technical and non-technical stakeholders, including executives.
Education
- Bachelor's degree in a quantitative field (e.g., CS, Statistics, Mathematics, Economics) or equivalent experience.
- Advanced degree (MS/PhD) in a quantitative discipline preferred for senior roles.
Benefits
- Base salary plus RSUs, bonus, and ESPP
- Medical, dental, and vision insurance
- Life insurance and other standard benefits
- Unlimited PTO
- 401(k) retirement plan
- Reasonable accommodations during recruitment for candidates with disabilities or other needs
Average salary estimate
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