Staff Machine Learning Scientist (Recommendations) at Depop

London, England, United Kingdom

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

E-commerce, Fashion, MarketplaceIndustries

Requirements

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 deployment, 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

Responsibilities

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

Skills

Machine Learning

Recommender Systems

Scalable ML

Model Development

Technical Leadership

Mentoring

Depop

Peer-to-peer marketplace for unique fashion items

About Depop

Depop is an online marketplace designed for buying, selling, and discovering unique fashion items, including designer pieces, vintage finds, streetwear, and sneakers. The platform operates as a peer-to-peer marketplace, allowing users to list their items for sale directly to other users. Depop generates revenue by taking a commission on each sale, which aligns the company's success with that of its sellers. The platform primarily targets younger consumers who value sustainability and often seek second-hand fashion items, making it an appealing option for those looking to reduce waste and support independent brands. Additionally, Depop fosters a sense of community among its users, encouraging interaction and connection, which enhances the buying and selling experience. This community-driven approach, along with its focus on unique and sustainable fashion, sets Depop apart from other online marketplaces.

London, United KingdomHeadquarters

2011Year Founded

$97.3MTotal Funding

ACQUISITIONCompany Stage

Consumer GoodsIndustries

1,001-5,000Employees

Risks

Increased competition from eBay may attract sellers away from Depop.

Marketplace fee for UK buyers may deter some users from Depop.

Generative AI development could disrupt traditional resale models, challenging Depop.

Differentiation

Depop targets younger consumers interested in unique, sustainable fashion items.

The platform fosters a community-driven approach, enhancing user experience and engagement.

Depop's commission-based model aligns its success with that of its sellers.

Upsides

Increased interest in sustainable fashion drives more consumers to Depop.

Social commerce rise creates new engagement opportunities with Depop's young audience.

U.S. resale market forecasted to grow, offering Depop significant growth potential.

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