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