Senior Product Manager - Ranking (ML) in London

Depop is a peer-to-peer circular fashion marketplace where anyone can buy, sell and discover secondhand fashion. Our mission is simple: to make fashion circular by making secondhand as exciting and rewarding as buying new.

Founded in 2011, Depop’s diverse community has helped move resale into the mainstream, where buying secondhand is no longer an alternative, but how people of different ages now engage with fashion. Today, more than 56 million registered users come to Depop to find great value, express their own personal style and give clothes a longer life.

We aim 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 hiring a Senior Product Manager (ML) to lead Depop’s Ranking team, responsible for the machine learning systems that determine which items buyers see across Depop. Our buyer mission is to create a deeply personal experience for our community by seamlessly matching their evolving preferences with our inventory.

The Ranking team sits at the heart of this mission, building the models, signals and optimisation strategies that power Search, Homepage and Recommendations, influencing millions of buying decisions every day.

Responsibilities:

• Own the ranking platform
• Own the ranking strategy across Search, Homepage and Recommendations, defining how machine learning models balance relevance, personalisation, diversity, freshness and marketplace objectives.
• Partner closely with ML Engineering, Applied Science and platform teams to continuously improve the quality, scalability and sophistication of our ranking systems.
• Set vision and strategy
• Define a clear product vision and strategy for ranking, shaping how machine learning matches buyers with inventory in ways that become increasingly personal, adaptive and effective.
• Partner with adjacent product and platform teams to ensure ranking capabilities are consistently applied across Search, Homepage and Recommendations.
• Decide what to build
• Prioritise investments across models, features, objectives, experimentation infrastructure and technical foundations.
• Use offline evaluation, online experimentation and marketplace analysis to make informed product decisions, balancing short-term performance with long-term capability building.
• Drive high-quality execution
• Partner with ML Engineers, Applied Scientists and Software Engineers to deliver production-quality improvements to ranking systems.
• Collaborate closely with Search, Recommendations and Experience teams to ensure ranking capabilities are effectively deployed across buyer experiences.
• Drive rigorous experimentation and evaluation, ensuring improvements translate into measurable gains in engagement, purchasing and marketplace outcomes.

What we're looking for:

• Significant experience building machine learning products, ideally in ranking, recommendations, search, advertising or personalisation.
• Strong technical understanding of modern ML systems and experience working closely with ML Engineers and Applied Scientists.
• Experience prioritising work across models, data, infrastructure and experimentation.
• Strong understanding of experimentation, offline evaluation and production ML metrics.
• Ability to translate complex technical trade-offs into clear product decisions.
• Comfortable operating in highly technical environments with significant ambiguity.
• Excellent communication skills with the ability to influence senior technical stakeholders.
• High ownership and accountability for business outcomes.

It would be a bonus if you have:

• Previous experience as a Machine Learning Engineer, Data Scientist, Applied Scientist or Software Engineer, or equivalent technical depth gained through extensive experience working on ML products.
• Experience with learning-to-rank, retrieval systems, recommender systems or search.
• Experience working on marketplace, ecommerce or other large-scale consumer products.

Additional Information:

• Health + Mental Wellbeing: PMI and cash plan healthcare access with Bupa, subsidised counselling and coaching with Self Space, Cycle to Work scheme, Employee Assistance Programme (EAP) for 24/7 confidential support.
• Work/Life Balance: 25 days of annual leave with the option to carry over up to 5 days, impact hours, fully paid 4-week sabbatical after completion of 5 years of consecutive service.
• Flexible Working: MyMode hybrid-working model with Flex, Office-Based, and Remote options.
• Family Life: 20 weeks of paid parental leave for full-time regular employees for birth parents, 12 weeks for non-birth parents, IVF leave, shared parental leave, and paid emergency parent/carer leave.
• Learn + Grow: Twice-yearly development chats and yearly performance reviews, learning budget, upskilling our employees with company-wide training workshops.
• Your Future: Life Insurance (financial compensation of 3x your salary), pension matching up to 6% of full base salary with Aviva.
• Depop Extras: In-office Depop Shop and a packing station with free delivery.