Principal Data Scientist, Delivery Technology

As a Principal Data Scientist at Gopuff, your primary responsibilities will include collaborating with various teams to create data science solutions, optimizing delivery experiences using machine learning models, and measuring the impact of these solutions on our business metrics. You'll lead projects focused on dispatch optimization, delivery routing, and partner experience, ensuring a seamless integration of data science into our operations.

To qualify for the Principal Data Scientist role at Gopuff, you should have over 5 years of experience in data science, particularly in the delivery technology industry. A Master’s or PhD in a quantitative field such as Operations Research, Mathematics, or Computer Science is required. Additionally, expertise in machine learning, strong Python and SQL skills, and excellent communication abilities are essential.

In your role as a Principal Data Scientist at Gopuff, you will work on projects aimed at enhancing the delivery experience for users. This includes developing algorithms for delivery routing optimization, creating predictive models for ETAs, and analyzing data to improve driver pricing and incentives, thereby directly impacting customer satisfaction.

Yes, Gopuff offers a hybrid working environment for the Principal Data Scientist role. While based in our approved hub markets, you can work remotely, providing flexibility while still allowing for collaborative work sessions with your team in person.

The culture at Gopuff is innovative, collaborative, and inclusive. As a Principal Data Scientist, you’ll be part of a team that values problem solving and creativity. Gopuff encourages diverse perspectives and fosters an environment where all employees can thrive and contribute towards changing the e-commerce landscape.

In responding to this question, draw on specific projects where you've utilized machine learning techniques. Highlight your approach to model selection, the algorithms used, and the results achieved, demonstrating your ability to apply theoretical knowledge to real-world challenges.

Discuss your approach to task prioritization, emphasizing effective communication with stakeholders to identify urgent needs. Explain how you use data-driven strategies to assess project impacts and how you adjust priorities to meet business goals.

Explain your mentorship philosophy, focusing on fostering a supportive learning environment. You might mention setting up regular one-on-one meetings, providing constructive feedback, and encouraging junior scientists to take on challenges that promote their growth.

Provide an example of a specific situation where you navigated ambiguity by defining clear goals. Discuss your process of gathering relevant data, involving stakeholders, and iteratively refining the problem statement until actionable insights were developed.

Share your preferred tools, such as Python libraries (like Pandas or scikit-learn) and SQL for data manipulation, as well as visualization tools like Tableau or Matplotlib. Illustrate how these tools enhance your data analysis processes.

Choose a project where you implemented a data science solution and detail the methodology you used for measuring its impact. Discuss any A/B testing or other experimental techniques you applied and how the results influenced future decisions.

Reflect on current trends such as the rise of automated machine learning, advancements in AI ethics, or the increased importance of transparency in algorithms. Discuss how these trends could impact the role of a data scientist and industry practices.

Outline your process for data cleaning and validation. Mention techniques you use to check for missing values, outliers, and duplicates, and explain how you integrate robust data governance practices into your projects.

Be specific about the challenge, focusing on the analytical techniques you used to approach the problem. Discuss the insights you gained and how they helped in guiding business decisions or optimizing processes.

Mention your dedication to continuous learning through reading research papers, participating in online courses, and engaging with the data science community on platforms like GitHub or LinkedIn. This shows your commitment to professional growth in a rapidly evolving field.