Senior Data Scientist

As a Senior Data Scientist at Jerry, you will dive into analytics to drive our customer acquisition strategies. You'll analyze data, build attribution models, and design experiments that help identify efficient growth channels. Your insights will directly influence business decisions as we work to scale our customer base and disrupt the car ownership market.

To apply for the Senior Data Scientist role at Jerry, you should have a Bachelor’s degree in a quantitatively rigorous field, a few years of consulting experience (preferably with MBB firms), and a strong background in customer acquisition and performance marketing. Proficiency in SQL and Python (or similar ML programming languages) is also essential.

At Jerry, the work environment is fast-paced, innovative, and team-oriented. As a Senior Data Scientist, you’ll collaborate with top talents across various departments to share insights and enhance our customer acquisition efforts. You'll thrive in a talent-dense setting that encourages personal growth and career advancement.

The Senior Data Scientist at Jerry is crucial in designing and analyzing A/B experiments and building predictive models that uncover the best channels for customer acquisition. Your data-driven insights will guide marketing investments and inform strategies that drive significant growth.

The company culture at Jerry is characterized by teamwork, innovation, and a commitment to excellence. Data teams are encouraged to continuously learn and push their boundaries while benefitting from a collaborative environment filled with passionate colleagues who are dedicated to solving real-world problems.

As a Senior Data Scientist at Jerry, you'll have ample opportunities for professional growth through exposure to diverse projects and collaboration with experienced leaders. The company is rapidly scaling, and your contributions can significantly impact our trajectory, offering pathways to leadership roles in the future.

In the Senior Data Scientist role at Jerry, you will work with SQL and Python, alongside various data visualization tools. You will leverage machine learning techniques to build predictive models, run experiments, and analyze large datasets while collaborating with other teams to translate analytics into actionable strategies.

When discussing your experience with data analysis and predictive modeling, share specific projects where you've applied statistical methods to extract insights. Highlight any specific tools you used, like Python or SQL, and explain how your work contributed to decision-making and strategy development.

In your response, emphasize your understanding of the A/B testing process, including hypothesis formulation, experiment design, data collection, and analysis. Discuss how you've addressed challenges or biases in prior tests and how you interpreted the results to inform strategic decisions.

Explain your experience with user acquisition channels and strategies you've devised based on analytical data. Provide examples of past successes, including specific metrics you used to measure your impact and the outcomes achieved as a result of your insights.

When answering, narrate a specific example where you faced a challenging data problem. Discuss your thought process, the techniques or models you used to analyze the data, how you identified a solution, and the final results that were achieved, illustrating your impact.

Highlight your communication skills and adaptability when conveying complex data insights. Mention your approach to tailoring messages for different audiences, using visuals like dashboards or presentations to help non-technical stakeholders grasp key takeaways. Emphasize collaboration in developing actionable insights.

Be specific about your level of proficiency in SQL. Talk about the types of queries you’ve written, datasets you've worked with, and how you’ve used SQL for data extraction and manipulation. If applicable, mention how this expertise has helped optimize processes or informed strategies within your previous roles.

Share a situation where your findings led to an unexpected conclusion that required a strategic pivot in your approach. Discuss how you adapted your analysis, redefined your objectives, and engaged with your team to implement new strategies based on your discoveries.

When replying, spotlight any specific machine learning algorithms you've applied, such as regression techniques, clustering, or classification methods. Describe projects where these algorithms were fundamental in driving user acquisition strategies and the outcomes derived from those efforts.

Discuss your strategies for continuous learning, such as attending industry conferences, participating in online courses, reading relevant publications, or engaging with data science communities. Share specific resources you rely on to stay informed and discuss how these have influenced your work.

Express your enthusiasm for the role by connecting it to Jerry’s mission to innovate the car ownership experience. Highlight the unique opportunities to leverage data in a fast-growing startup environment and your eagerness to contribute to meaningful projects that can drive significant impact.