We are looking for Research Scientists to join the Personalization research group to conduct research in core machine learning and its applications to recommendation, search, speech, natural language understanding, algorithmic fairness, accountability, transparency. Candidates will have experience in developing and publishing new fundamental methods in areas such as reinforcement learning, approximate inference, causal inference, deep learning, time series modeling, meta-learning, and probabilistic modeling.
What you’ll do
- You will apply your scientific knowledge and research skills to understand and develop new methods in machine learning and their applications to areas such as recommendation, search, speech and language understanding, and related areas to music streaming.
- You will work in collaboration with other scientists, engineers, designers, user researchers, and analysts across Spotify to design creative solutions to challenging problems.
- You will design scientific experiments, analyze product engagement data, gather and process large data sets to support your research.
- You will work on projects that cut across Spotify’s organization, including areas such as product, marketing, and content.
- You will have product impact, while working on and further develop a long-term research roadmap.
- External engagement such as publishing, giving talks, and being an active community member at top machine learning and information retrieval conferences is encouraged.
Who you are
- You have a PhD in computer science, computational statistics, or related area.
- You have publications in conferences such as NIPS (NeurIPS), ICML, ICLR, UAI, AISTATS, and journals such as JMLR or Machine Learning, or related.
- You have a demonstrated interest and facility in applying ML to large data sets from recommendation, music, search, speech, natural language, or related.
- You are a creative problem-solver who is passionate about digging into complex problems and devising new approaches to reach results.
- You have experience with the complexities of real-world data, and understand the value of both in-depth, qualitative and web-scale, quantitative data working together to create a deep understanding of machine learning algorithms.
We are proud to foster a workplace free from discrimination. We strongly believe that diversity of experience, perspectives, and background will lead to a better environment for our employees and a better product for our users and our creators. This is something we value deeply and we encourage everyone to come be a part of changing the way the world listens to music.