Data Scientist

Role Overview

Join a high-impact product team as a Data Scientist focused on building and improving next-generation consumer products. You’ll partner with Product Managers, Engineers, other Data Scientists, and cross-functional stakeholders to solve business problems using data and experimentation, and to influence product strategy.

Responsibilities

• Analyze large, complex datasets to identify trends, opportunities, and drivers of business performance
• Design, execute, and evaluate experiments, including A/B testing and other measurement methodologies
• Develop statistical models and analytical frameworks for forecasting, optimization, and strategic planning
• Define KPIs and measurement frameworks for product initiatives
• Build dashboards, reports, and reproducible analyses to monitor product performance
• Translate business questions into analytical solutions with Product/Engineering/Business stakeholders
• Present findings and recommendations to technical and non-technical audiences
• Ensure data quality, validate analytical outputs, and document methodologies

Requirements

• BS or MS in a quantitative field (Statistics, CS, Data Science, Mathematics, Economics, etc.)
• Strong hands-on Python and SQL for large-scale data analysis
• Solid statistics foundation (hypothesis testing, regression, experimental design, causal inference)
• Experience designing/analyzing A/B tests and experimentation frameworks
• Experience building dashboards and communicating insights with clear data storytelling
• Ability to work independently in a fast-paced, cross-functional environment while managing multiple priorities

Preferred Qualifications / Nice to Haves

• Experience with 0-to-1 new product development
• Background in product analytics, experimentation, or decision science
• Experience with ETL/data pipelines or workflow automation
• Prior experience in a large technology company or product-focused organization

Preferred Technical Skills

• Python libraries: Pandas, NumPy, scikit-learn
• BI tools such as Tableau
• Statistical modeling, experimentation (A/B testing), forecasting, KPI development
• Data visualization and data pipelines