We are seeking a data scientist to join the User Fraud R&D Studio. Our mission is to protect Spotify from unwanted behaviour by detecting and preventing fake account creation and artificial streaming activity across all business areas. You will join a cross-functional team of data scientists and machine learning engineers who continuously experiment, iterate and deliver new fraud prevention solutions. Together, we address a diverse and evolving set of challenges, study user behaviour, develop data science and machine learning solutions, and bring insights into every decision we make. Your work will directly impact our ability to identify and prevent fraud.
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What You'll Do- Analyze user and system data to detect and assess risks of artificial or fraudulent activity on Spotify.
- Build data science and machine learning solutions to enable Spotify to quickly and automatically analyze data for fraud prevention.
- Communicate insights and recommendations to non-technical audiences using clear visuals and data storytelling.
- Build scalable data pipelines and dashboards to track our performance and support decision-making.
Who You Are- At least two years of experience working with large, complex datasets using Python, SQL or R.
- You should also be comfortable creating your own data visualizations using tools like matplotlib, ggplot, looker studio or similar.
- Degree in data science, computer science, statistics, economics, mathematics, or a similar quantitative field.
- Experience and strong understanding of working with data pipelines, anomaly detection methods, statistical modeling and machine learning.
- Strong analytical skills, with the ability to turn data into actionable insights and recommendations.
- Strong problem-solving skills, intellectual curiosity, and a proactive approach to identifying new opportunities for fraud detection and prevention.
- This role is based in Stockholm, Sweden
- We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home. We ask that you come in 3 times per week.
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