Senior Manager of Product Analytics

The Senior Manager of Product Analytics at SpotOn is tasked with leading a team of analysts and data scientists, collaborating with Product, Engineering, and Design to integrate data into product decisions. Key responsibilities include developing measurement frameworks, analyzing customer behavior, executing A/B tests, and providing actionable insights that shape product strategies and drive business growth.

Candidates for the Senior Manager of Product Analytics role at SpotOn should possess over 7 years of experience in product analytics, business intelligence, or data science, with at least 3 years in a management position. A strong background in SQL, statistical analysis, and experience with various business intelligence tools is essential. A Bachelor’s or Master’s degree in a quantitative field is required, and an MBA is a bonus.

SpotOn is dedicated to employee growth, offering a competitive benefits package that includes tuition reimbursement of up to $2,000 per calendar year. This investment in professional development ensures that the Senior Manager of Product Analytics has opportunities to advance their skills and knowledge, benefiting both the employee and the company.

The ideal candidate for the Senior Manager of Product Analytics at SpotOn should be proficient in SQL and dbt and familiar with business intelligence tools such as PowerBI, Metabase, Google Analytics, and Amplitude. Familiarity with statistical analysis and A/B testing methodologies is also crucial for effectively measuring product performance and customer experience.

The base salary range for the Senior Manager of Product Analytics at SpotOn starts at $150,000 and can go up to $200,000. This range reflects the candidate's experience and geographical location, in addition to a comprehensive compensation package that includes stock options and numerous benefits.

When discussing your experience with A/B testing, focus on specific examples where you designed and executed tests. Explain the hypotheses you tested, the metrics you used to measure success, and how your findings influenced product decisions. Illustrate your role in translating results into actionable recommendations.

In your response, highlight the importance of collaborating with cross-functional teams. Discuss how you ensure rigorous analysis underpins product strategies by providing insights based on user behavior and feedback. Mention specific frameworks you might have implemented to streamline this integration.

Share a specific anecdote detailing your leadership style. Discuss how you fostered a culture of curiosity and collaboration, and the strategies you used to empower your team. Highlight any measurable outcomes your team achieved under your leadership.

Discuss a specific challenge involving data discrepancies or inaccuracies. Explain how you identified the issue, the tools you employed to correct it, and how you established processes to prevent similar issues in the future. Emphasize your problem-solving skills.

Illustrate your proficiency with statistical methods and any hands-on experience you have with machine learning. Provide examples of projects where you utilized these skills, and elaborate on the insights derived from your analysis and how they informed business strategies.

Emphasize the importance of data storytelling in connecting with non-technical stakeholders. Discuss your approach to translating complex data insights into clear, actionable narratives and how this has influenced key decision-making processes in your previous roles.

Identify the key metrics relevant to product performance, such as user engagement, conversion rates, or customer satisfaction scores. Explain why you prioritize these metrics and how you leverage them to analyze product success and guide future strategy.

Discuss your approach to efficiency in analytics. Detail any frameworks or processes you use to prioritize quick wins while ensuring comprehensive analysis. Highlight how you measure and communicate trade-offs between speed and depth of analysis.

Share your strategies for staying informed about industry trends, such as following relevant journals, attending webinars, or participating in professional communities. Highlight how you apply this knowledge to enhance your team's methodologies and tools.

Explain the importance of collaboration with product and engineering teams for effective analytics. Discuss how you establish effective communication channels, share insights, and align on product priorities to integrate analytics seamlessly into the development lifecycle.