Senior Manager, Data Science, Analytics - Notifications, Consumer Growth

The Senior Manager of Data Science, Analytics - Notifications at DoorDash is tasked with leading a talented team while developing and driving deep analyses to uncover key business drivers. This role requires directing the data science strategy for notifications, influencing product roadmaps, interfacing with senior stakeholders, and establishing measurement strategies for success metrics.

To be qualified for the Senior Manager, Data Science, Analytics - Notifications position at DoorDash, candidates should have at least 4 years of experience managing data scientists and 9 years in Data Science and Product Analytics, particularly in related industries. Strong technical skills, expertise in A/B experimentation, and proficiency in data pipelines are also essential.

The Senior Manager contributes significantly to DoorDash's consumer growth by leveraging data analysis to enhance notification strategies that reach and engage users effectively. By developing actionable insights and recommendations, this position directly influences user experiences and retention, fostering sustainable growth for the platform.

A Senior Manager, Data Science, Analytics - Notifications at DoorDash should be familiar with data analysis and visualization tools alongside statistical software often used for A/B testing. Knowledge of data pipelines and experience in integrating generative AI in analytics can also be highly beneficial for this role.

The culture at DoorDash emphasizes growth, innovation, and inclusivity. As a Senior Manager in Data Science, you will collaborate with diverse teams, showcasing your insights to influence decision-making while enjoying the support of a dynamic work environment that values employee well-being and diversity.

In your response, share specific examples of how you've led data science teams, discussing your leadership style and any successful projects conducted under your management. Highlight key metrics that demonstrate how your team made a tangible impact on business outcomes.

Discuss your methodology for leveraging data in decision processes, emphasizing how you prioritize projects based on analysis and findings. Provide an example of a decision in your previous role that was enhanced by data insights.

Explain your approach to A/B testing, including how you design experiments and analyze the results to make informed recommendations. Share specific scenarios where you applied these techniques to drive growth or improve user engagement.

Illustrate your strategies for fostering effective communication and collaboration between technical and non-technical teams. Highlight instances where you've successfully bridged gaps to achieve project goals.

Share how you integrate statistical analysis in data science projects, providing examples of theories or methodologies you frequently apply. Discuss any complex analyses you conducted and how they influenced your strategic decisions.

Talk about your time management strategies, how you prioritize tasks, and communicate with stakeholders during crunch time. Provide an example where you successfully delivered analysis under a demanding deadline.

Reflect on a time when your analytical insights significantly influenced a product decision or strategy. Be sure to describe the context, your findings, and the outcomes of the changes that were made.

Discuss your methods for continuous learning, such as attending workshops, participating in online courses, or following industry publications. Highlight any current trends you find exciting and how they could potentially impact your work.

Explain your understanding of generative AI and its applications in data science. Provide examples of projects where you have utilized or considered using AI to enhance analytical processes or consumer interactions.

Discuss the metrics and KPIs you track to assess project success. Explain how you use this data to provide feedback to your team, adjust strategies, and share results with senior leadership.