Research Scientist - Advanced Causal Inference

Spotify; London; Remote; GBP 60,000 - 90,000; Full time

Job summary

A leading company is seeking a Research Scientist specializing in causal inference and machine learning. This role involves conducting innovative research, collaborating with interdisciplinary teams, and contributing to the research community through publications. Candidates should have a Ph.D. in relevant fields and experience with programming languages and data analysis.

Qualifications

• Ph.D. degree in relevant fields or equivalent experience.
• Publications in relevant communities.

Responsibilities

• Conduct research in causal inference and machine learning.
• Collaborate with teams to improve decision-making.

Skills

Problem-Solving

Creativity

Education

Ph.D. in Computer Science

Ph.D. in Physics

Ph.D. in Mathematics

Ph.D. in Engineering

Tools

Python

R

SQL

TensorFlow

PyTorch

Scikit-learn

CausalML

EconML

Ray

Job description

Research Scientist - Advanced Causal Inference

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.
• Participate in innovative fundamental and applied research in causal inference, machine learning, and related fields.
• Apply scientific knowledge to analyze and collect data, perform analyses, identify problems, devise solutions, construct methodologies, including metrics and best practices, and conduct experiments to validate these.
• 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.
• You are 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 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

• We offer flexibility to work where you work best! For this role, you can be within the EMEA region, provided we have a suitable work location.
• This team operates within the Central European and GMT time zones for collaboration.
• This role is ideally located in the UK, Sweden, or potentially other EMEA locations, excluding France due to on-call restrictions.

About the company

Spotify is all the music you’ll ever need.