We are looking for Research Scientists to join the Personalization research group to conduct research on the intersection of music recommendation, search, natural language understanding, and algorithmic fairness, accountability and transparency. You will have experience in developing and publishing new results and methods in areas such as algorithmic accountability, people’s interaction with Machine Learning-based systems, and/or representation in data.
What you’ll do
- You will apply your scientific knowledge and research skills to understand and develop new methods in assessing and addressing algorithmic bias.
- You will work on practical applications such as recommendation, search, voice 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 developing a long-term research roadmap.
- External engagement such as publishing, giving talks, and being an active community member at top conferences is encouraged.
Who you are
- You have a PhD in human-computer interaction, computational social science, machine learning, natural language processing or related area.
- You have publications in venues such as FAT*, CHI, CSCW, WWW, HRI, RecSys, SIGIR, NeurIPS, or related.
- You have a demonstrated interest in algorithmic bias, and accountability, and the translating of related research into practice.
- You have a demonstrated interest in personalized recommendations, crowdsourcing, diversity in AI – and music.
- You are intrigued by how interaction design, data collection strategies, organizational decisions, and people’s perceptions affect Machine Learning outcomes.
- 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 people’s interaction with technology.
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.