Staff Machine Learning Scientist (Recommendations)

Staff Machine Learning Scientist (Recommendations)

Team: Engineering & Data

Location: Depop - London

Company Description

Depop is the community-powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make fashion circular, redefining fashion consumption. Founded in 2011, the company is headquartered in London, with offices in New York and Manchester, and in 2021 became a wholly-owned subsidiary of Etsy. Find out more at www.depop.com Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome, no matter who they are or where they’re from. Just as our platform connects people globally, we believe our workplace should reflect the diversity of the communities we serve. We thrive on the power of different perspectives and experiences, knowing they drive innovation and bring us closer to our users. We’re proud to be an equal opportunity employer, providing employment opportunities without regard to age, ethnicity, religion or belief, gender identity, sex, sexual orientation, disability, pregnancy or maternity, marriage and civil partnership, or any other protected status. We’re continuously evolving our recruitment processes to ensure fairness and are open to accommodating any needs you might have. If, due to a disability, you need adjustments to complete the application, please let us know by sending an email with your name, the role to which you would like to apply, and the type of support you need to complete the application to . For any other non-disability related questions, please reach out to our Talent Partners.

Life is about creating. That's why we're home to over 30 million artists, stylists, designers, sneakerheads — and you? We're the community-powered, circular-minded marketplace changing the world of online fashion. Now it's time to get inspired at Depop.

Responsibilities

Job description

The Recommendations team builds models that power discovery at Depop, helping millions of users find items that they will love. As a Staff Machine Learning Scientist, you’ll set the technical vision for our next-generation recommendation models, lead high-impact initiatives, and mentor others to drive innovation at scale.

Responsibilities

You will:

Lead the design and deployment of advanced recommendation systems, encompassing encoder-based architectures, vector representations and large-scale retrieval.

Mentor, coach, and set technical direction within the Recommendations team, helping others grow and innovate.

Collaborate closely with cross-functional partners (product, engineering, data) to define problems, translate them into scalable solutions, and deliver measurable business outcomes.

Lead the end-to-end lifecycle of ML projects: from ideation, data acquisition, feature engineering, training, and evaluation to deployment and ongoing iteration.

Drive innovation in recommendation systems by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy.

Act as a thought leader in the recommendations space, sharing learnings internally, engaging with the wider ML community, and showcasing our work externally.

Qualifications

Proven track record in designing, deploying, and optimizing large-scale recommendation systems, including candidate retrieval and ranking models, with measurable impact in production environments.

Deep understanding of machine learning fundamentals and applied experience with architectures including collaborative filtering, deep learning, and hybrid recommendation approaches.

Proven ability to productionize ML models and pipelines: from prototyping to de

ployment, with strong experience in monitoring, iteration, and troubleshooting.

Advanced programming skills in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or similar.

Solid foundation in stats, experimental design, and working with offline/online evaluations in real-world settings.

Experience leading projects and mentoring engineers or scientists, with a track record of fostering team growth and technical excellence.

Excellent communication skills: able to bridge technical and non-technical stakeholders and influence decision making.

Committed to responsible AI practices, including attention to ethics, fairness, and inclusivity.