Data Scientist (User Growth) - job 1 of 6

As a Data Scientist (User Growth) at Jerry, you'll be responsible for driving user acquisition strategies through deep data analysis. This involves running A/B experiments, building reports and dashboards, and developing predictive models to monitor marketing channel performance. You will work collaboratively with various stakeholders to translate insights into actionable growth marketing decisions.

To be considered for the Data Scientist (User Growth) role at Jerry, you should have a Bachelor’s degree in a quantitatively rigorous discipline and at least 3 years of experience in consulting, data science, or growth analytics. Comfort with SQL and Python is essential, as well as an analytical mindset and the ability to communicate complex insights across different team levels.

The Data Scientist (User Growth) contributes significantly to Jerry's mission by analyzing data that shapes customer acquisition strategies. By identifying efficient acquisition channels and running experiments, you will provide actionable insights that help Jerry grow from 5M to 50M customers and ultimately disrupt the car ownership market.

At Jerry, the work environment is dynamic, collaborative, and highly innovative. You'll be immersed in a talent-rich atmosphere, where you'll work alongside experienced leaders and your peers within a culture that embraces curiosity and problem-solving. Expect to face challenges that encourage professional growth while making a significant impact on the business.

The Data Scientist (User Growth) plays a crucial role in generating revenue for Jerry by leveraging data to drive customer acquisition. By developing key metrics and insights related to marketing performance and identifying profitable growth channels, your work will directly influence the company’s trajectory toward a $5B valuation.

In responding to this question, highlight a specific project where your data analysis led to a successful outcome. Discuss the problem you were addressing, the data you used, the insights you gained, and how those insights influenced actions that drove user acquisition or retention.

When discussing A/B testing, focus on your systematic approach: defining hypotheses, selecting the right sample size, and identifying metrics that matter most. Emphasize the importance of deriving actionable insights from the results to optimize future strategies.

Attribution modeling is critical in understanding which channels contribute to user acquisition. Explain how your experience with different models has helped quantify the impact of various marketing touchpoints and how that knowledge aids in budget allocation decisions.

Discuss the specific programming languages and tools you are proficient in, such as SQL, Python, or business intelligence platforms. Highlight how you’ve used these tools to conduct analyses and derive valuable insights.

In answering this question, provide an example of a situation where you effectively presented data insights to a mixed audience. Focus on your communication strategy, simplifying complex concepts without losing key information, ensuring stakeholders understood the implications.

Talk about the various data sources you have used, such as customer behavior data, marketing performance metrics, and demographic information. Explain how you leverage these data points for comprehensive analysis to inform growth strategies.

Explain your strategies for staying current in the field, whether it's through continuous learning, attending industry conferences, or participating in relevant online communities. Highlight specific resources you find helpful for professional development.

Discuss your meticulous approach to data cleaning and validation to ensure accuracy. Highlight techniques such as cross-validation, peer review, and the use of automated tools to minimize errors in analysis.

Share your strategies for resolving conflicts constructively. Emphasize active listening, seeking common ground, and adapting to feedback while still pushing for data-driven decisions to guide the team toward better results.

Discuss your background in predictive modeling, including specific projects where you have built models to project user behavior or customer trends. Describe the outcome and how those models influenced strategic decisions in user growth.