Senior Research Data Scientist, Central Operations Analytics

Minimum qualifications:

• Master's degree in statistics, data science, mathematics, physics, economics, operations research, engineering, a related quantitative field, or equivalent practical experience.
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
  
• Experience using ETL tools.
• Experience conducting root cause analysis.

Preferred qualifications:

• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
  
• Experience supporting manufacturing, supply chain, or quality analytics.

About the job

The Central Operations Analytics team's (COAT) mission is to improve quality and manufacturing through data-driven decision-making. The team focuses on governing quality return rates forecast through in-house developed algorithms, owning the strategy and development of scalable data tools, prototyping GenAI and advanced dashboards, and leading analytics to solve critical business problems.

The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  
• Use custom data infrastructure or existing data models as appropriate. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
  
• Develop specialized tools for root cause analysis, station diagnostics, and statistical process control (SPC) to enable continuous improvement.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality.
  
• Analyze telemetry data and customer sentiment to quantify its impact on quality and inform design improvements.