What You Will Do
- Build statistical, optimization, and machine learning models for applications including pricing, targeting, and experimentation.
- Work with engineers and product managers to turn data science prototypes into robust, reliable solutions.
- Use data to understand product performance and to identify improvement opportunities.
- Solve ambiguous, challenging business problems using data-driven approaches.
- Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
- Develop new methodologies for data science including modeling, coding, analytics, optimization, and experimentation.
- Collaborate with cross-functional teams such as product, engineering, operations, and marketing to drive system development end-to-end from conceptualization to final product.
Basic Qualifications
- Ph.D., M.S. or Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- 6+ years of industry experience as an Applied or Data Scientist or equivalent.
- Proficiency in programming languages (Python, Java, Scala) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn),
- Solid understanding of MLOps practices, including design documentation, testing, and source code management with Git.
- Advanced skills in the development and deployment of large-scale ML models and optimization algorithms
- Experience in developing causal inference methodologies and experimental design (e.g., A/B and market-level experiments)
- Strong business and product sense: ability to shape vague questions into well-defined analyses and success metrics that drive business decisions.
Preferred Qualifications
- Expertise in developing causal inference methodologies, experimental designs, and advanced analytical methods.
- Strong experience in building a wide range of models (e.g. causal inference, optimization, ML) for business applications.
- Experience in algorithm development and rapid prototyping.
- Design, develop, and operationalize econometric models to assess challenging causal problems such as product incrementality and long-term value
- Propose, design, and analyze large scale online experiments and interpret the results to draw actionable conclusions.
- Ability to drive clarity on the best modeling solution for a business objective
- Collaborate with cross-functional teams across disciplines such as product, engineering, and operations to drive system development end-to-end from generating ideas to productionizing.
For San Francisco, CA-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year.
For Seattle, WA-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year.