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Spotify’s mission is to unlock the potential of human creativity. We are looking for a Research Scientist specialising in causal inference and machine learning to help us with this mission. Successful applicants are encouraged to conduct research in causal inference and apply causal inference techniques to craft and build tools to help creators and Spotify teams make better decisions. This is an opportunity to improve decision making with causal inference by collaborating with multiple teams to reshape Spotify’s existing products and develop new ones.
Our team is interdisciplinary, focusing on ensuring that the foundations of Spotify technologies are at or above the groundbreaking. In the process we aim to redefine and improve the state-of-the-art for the field and contribute to the wider research community by publishing papers.
What You'll Do
- Join an interdisciplinary team passionate about making every user and creator interaction with Spotify outstanding and in the process pushing innovation and contributing to the wider research community by publishing papers
- You will participate in innovative fundamental and applied research in causal inference, machine learning, and related fields
- You will apply your scientific knowledge to analyze and collect data, perform analyses, identify problems, devise solutions and construct methodologies, including metrics and best processes, and conduct experiments to validate these
- You will be a valued member of an autonomous, cross-functional team working in collaboration with other scientists, engineers, product managers, designers, user researchers, and analysts across Spotify to craft creative solutions to challenging problems
- External engagement such as publishing, giving talks, and being an active community member at top conferences is actively encouraged
Who You Are
- You have a Ph.D. degree in Computer Science, Physics, Mathematics, Engineering, with a focus on fundamental or applied causal inference, or equivalent experience. Previous proven industry experience is a plus
- You have publications in relevant communities such as UAI, CLeaR, ICML, ICLR, NeurIPS, AAAI, WWW, KDD, or related
- A problem-solver with experience with Python, R, or similar languages. Experience with tools like CausalML, EconML, TensorFlow, PyTorch, Scikit-learn, Ray, etc., is a strong plus
- You have experience with hands-on skills in sourcing, cleaning, manipulating, analysing, visualising and modelling of real data. Experience with SQL is a plus
- You are a creative problem-solver who is passionate about digging into complex problems and devising innovative ways to reach results
Where You'll Be
- This role is based in Stockholm (Sweden) or London (UK)
- 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
What you need to know about the London Tech Scene
London isn't just a hub for established businesses; it's also a nursery for innovation. Boasting one of the most recognized fintech ecosystems in Europe, attracting billions in investments each year, London's success has made it a go-to destination for startups looking to make their mark. Top U.K. companies like Hoptin, Moneybox and Marshmallow have already made the city their base — yet fintech is just the beginning. From healthtech to renewable energy to cybersecurity and beyond, the city's startups are breaking new ground across a range of industries.
