Data Science Manager, Global On-Platform Marketing

As a Data Science Manager at Spotify, your primary responsibilities include leading and mentoring a team of data scientists, collaborating on long-term roadmaps with marketing priorities, directing data infrastructure work, and delivering insightful analyses of on-platform marketing campaigns. You’ll create and communicate tailored recommendations to enhance marketing effectiveness while working closely with various teams.

To qualify for the Data Science Manager position at Spotify, candidates should have a Bachelor's degree in a relevant field like Economics or Statistics, a minimum of 5 years of Data Analytics experience, and at least 2 years of experience in managing data science teams. Familiarity with SQL, Python, data visualization tools like Tableau, and A/B testing methods is essential.

The Data Science Manager plays a crucial role in shaping Spotify's marketing strategy by analyzing campaign performance, defining metrics, and providing insights that inform decision-making. You'll support the Global On-Platform Marketing team in communicating effectively with users by ensuring that data-driven recommendations enhance campaign impact.

Valuable skills for a Data Science Manager at Spotify include strong analytical capabilities, experience with data pipeline architecture, excellent communication skills, and a deep understanding of experimentation methods like A/B testing. Additionally, having proficiency in machine learning and experience in consumer tech can set candidates apart.

Spotify offers a flexible work environment for its Data Science Manager, allowing options for remote work or in-office collaboration from their New York City location. You’ll operate within the EST timezone for effective teamwork, fostering a supportive and globally distributed culture where creativity and data-led insights are valued.

When asked about your experience leading a data science team, focus on specific examples where you managed the team dynamics, mentored junior team members, and how you defined the team's objectives to align with company goals. Highlight any key projects where your leadership made a significant impact.

In your response, emphasize the importance of data validation techniques you employ, such as cross-referencing datasets, implementing automated testing for data pipelines, and setting up consistent monitoring processes. Share specific tools or methods you've found effective in maintaining high data quality.

Discuss your approach to working closely with marketing and business stakeholders to establish clear goals for campaigns. Explain how you balance quantitative data with stakeholder inputs to define relevant metrics, and share examples of how those metrics influenced campaign decisions in past projects.

Use this opportunity to demonstrate your problem-solving skills and leadership qualities. Explain a specific project, the challenges faced, how you collaborated with others to find solutions, and the ultimate outcomes. Emphasize your ability to adapt and learn from experiences.

Mention your experience with data visualization tools like Tableau and how you use these tools to present data in an understandable manner for various stakeholders. Detail examples of complex data presentations you've created to provide actionable insights.

Share your personal strategies for continuous learning, such as taking online courses, attending conferences, or engaging with data science communities. Highlight any blogs, podcasts, or journals you follow that keep you informed about new algorithms, tools, and best practices.

Discuss your strategies for ensuring that technical analyses are translated into relatable insights. Talk about your experience tailoring your communication style based on the audience and how you use storytelling to make data relatable.

Talk about an A/B testing project you led, detailing the hypothesis, set up, metrics defined, and outcomes. Explain how your insights contributed to overall campaign success and the importance of experimentation in marketing decisions.

Articulate your understanding of machine learning and its applications in data analysis, such as feature engineering or forecasting. Provide an example of how you've used machine learning techniques in past projects and their impact on results.

Explain your prioritization process, which may include assessing project impact, stakeholder inputs, and team capacity. Describe tools or methodologies (like agile or Kanban) you use to stay organized and on track amid shifting priorities.