Data Science Manager, Merchant Analytics Lead

DoorDash; Austin (TX); On-site; USD 184,300 - 271,000; Full time

Benefits offered by this job

401(k) plan with employer matching

Paid parental leave

Wellness benefits

Paid time off and sick leave

Medical, dental, and vision benefits

Disability and life insurance

Job summary

DoorDash in Austin, Texas is seeking a Data Science Manager to lead a team of data scientists within the Merchant Analytics team. This role involves partnering with multiple departments to shape high-priority product and business decisions based on data-driven insights. Candidates should have over 6 years of experience in analytics or data science, proven leadership skills, and a strong analytical background. The successful applicant will contribute to measurable business outcomes through quality analytics and predictive modeling.

Qualifications

• 6+ years of experience in analytics, data science, or a related field.
• Proven experience in leading data scientists or analysts.
• Strong understanding of predictive modeling and translating data into business decisions.

Responsibilities

• Lead and develop a team of data scientists responsible for impactful analytics.
• Partner with various teams to influence business decisions.
• Define success metrics and build measurement frameworks.

Skills

Analytics

Data Science

Predictive Modeling

Decision Support

Cross-Functional Communication

Education

Bachelor's degree in a related field

6+ years of experience in analytics or data science

Job description

DoorDash in Austin, Texas is seeking a Data Science Manager to lead a team of data scientists within the Merchant Analytics team. This role involves partnering with multiple departments to shape high-priority product and business decisions based on data-driven insights. Candidates should have over 6 years of experience in analytics or data science, proven leadership skills, and a strong analytical background. The successful applicant will contribute to measurable business outcomes through quality analytics and predictive modeling.