At Spotify Advertising, our mission is to build the next-generation advertising platform for marketers that can scale the freemium experience for hundreds of millions of fans and tens of thousands of advertisers. This scale brings unique challenges and tremendous opportunities to define the product for our business. The advertising business is poised for substantive growth in the coming years and is an exciting place to be. Research and Insights is at the core of driving business decisions for this team.

Location

New York

Job type

Permanent

The Ads Business Data Science & Research team is seeking a senior-level data scientist to help build and maintain data pipelines, data science, and ML-based solutions focused on driving advertiser and revenue growth in Spotify Advertising. One of your key focuses will be supporting the adoption and implementation of generative AI and ML models built to unlock ad creative insights on a global scale. You will work alongside Engineers, Research Economists, and business analysts by leading creative insights from a Data Science perspective.

As part of the Advertising Business team, you’ll work on the latest advertising technologies to connect millions of brands to billions of fans. We’re paving the way for a sustainable global Spotify business that allows even more creators to make a living off their art. Join us and help build the business team that empowers this!

What You'll Do

Partner with business partners to understand problems space and reframe them as data science solutions end-to-end.
Deliver challenging, large-scale problems with complexity and ambiguity independently
Write code (Python, R, SQL, etc.) to obtain, manipulate, and analyze large datasets
Build statistical models and design experiments to solve applicable business problems
Communicate data-driven insights and recommendations to key partners
Work closely with a cross-functional team of economists, engineers, product owners, and business decisions makers
Prioritize business solutions, regardless of product area

Who You Are

You have 5 or more years of experience in a similar data science role and a degree in economics, statistics, or similar quantitative subject areas
You are comfortable building and maintaining large, complex datasets
You have the technical competence to perform more advanced analytics using your coding skills for analytics and data extraction (such as SQL, Python, R)
You have experience in causal inference and experimental design
Broad knowledge of ML methods, statistical analysis, and problem-solving skills
You have experience taking a project idea and bringing it to life through self-driven scoping, planning, and stakeholder communication
You have built statistical models with proven business impact
You can explain complex concepts to non-technical stakeholders and conduct nuanced discussions with engineers
Ability to collaborate with DEs, MLEs, and Research Scientists
Experience with LLMs
Bonus if you have experience analyzing ad creatives or ad tech

Where You'll Be

We offer you the flexibility to work where you work best! For this role, you can be within the Americas region as long as we have a work location.
This team operates within the Eastern time zone for collaboration.

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Our global benefits

Extensive learning opportunities, through our dedicated team, GreenHouse.

Flexible share incentives letting you choose how you share in our success.

Global parental leave, six months off - fully paid - for all new parents.

All The Feels, our employee assistance program and self-care hub.

Flexible public holidays, swap days off according to your values and beliefs.

Learn about life at Spotify

The United States base range for this position is $143,023 to $204,319, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.