Job Description
Digital Product at ASOS
At ASOS,we’rebuilding the future of fashion discovery. Serving millions of customers globally, our product and tech teams power experiences that make shopping intuitive, inspiring, and deeply personal.
Across Search & Discovery, our ambition is to create a journey that feels uniquely tailored to each customer—anticipating needs, surfacing inspiration, and removing friction at every step.
Personalised recommendations sit at the heart of this vision—driving meaningful commercial impact while helping customers discover products and outfits they genuinely love.
About the Role
We’rehiring aSenior Product Managerto lead Recommendations & Personalisation at ASOS—owning the evolution of ourcore recommendations platform and key customer experiences. You willbe responsible for:
- Single-item product recommendations(e.g. “People also bought”)
- Thecore recommendations systempowering multiple touchpoints
- Outfit recommendations and AI‑driven styling experiences
Your mission is tobuild a unified personalisation systemthat enables true1:1 customerjourneysacross ASOS—balancing inspiration, relevance, and commercial outcomes. This is ahigh-impact, platform-leaning rolewhere you willoperateacross multiple product teams, shaping both the underlying systems and the customer-facing experiences they enable.
What You’ll Do
Own recommendations strategy and platform direction
- Define and lead the roadmap for ASOS’srecommendations ecosystem, spanning retrieval, ranking, and orchestration across surfaces
- Drive towards a unified personalisation systemacross homepage, PLP, PDP, bag, and CRM
- Balanceshort-term commercial optimisationwith long-term platform capability building
Deliver high-impact customer experiences
- Shape how recommendations show up across the shopping journey—ensuring they areintuitive, inspiring, and on-brand
- Partner closely with Product Design to create experiences thatenhance discovery without adding friction
- Elevate outfit recommendations as a core driver ofinspiration and basket building
Lead experimentation and data-driven decision making
- Define and run rigorousA/B tests and multi-variant experimentstovalidateideas and optimise performance
- Establish clearmetrics frameworks(conversion, AOV, engagement, profit) and use them to guide prioritisation
- Drive a culture ofevidence-based decision making, balancing online and offline evaluation insights
- Drive improvements indata foundations, observability, and real-time personalisation capabilities
Operate across teams and stakeholders
- Workhand-in-handwith ML teams onrecommender systems, ranking models, and personalisation logic
- Translate complex ML capabilities intoclear product decisions and measurable outcomes
- Lead across multiple squads, ensuringalignment and coherence across recs experiences and systems
- Partner with Commercial, Trading, Marketing, and CX teams to alignbusiness goals with customer value
- Navigate complex trade-offs betweenrevenue, profitability, availability, and experience quality
Qualifications
About You
Core experience
- Proven experienceworking withrecommender systems, ranking, or search-driven products
- Strongtrack recordofhands-on experimentation (A/B testing)and data-driven decision making
- Experience working closely withmachine learning / data science teamsto ship customer-facing features
- Delivery ofconsumer-facing products at scale, ideally within ecommerce or marketplaces
- Pragmatic builder—able tomove quickly from hypothesis tovalidatedoutcome
- Collaborative leader who caninfluence without authority across functions
Product craft and leadership
- Customer-obsessed and highly curious about how people discover products and make decisions
- Ability tooperateacross multiple teams and initiatives,connecting platform and experience
- Strong commercial instincts—able to balancecustomer value with revenue and profitability
- Skilled at turning complex systems intoclear strategy, roadmaps, and narratives
- Comfortable navigating ambiguity andleading in fast-moving, data-rich environments
Technical and domain understanding
- Familiarity with recommender approaches (e.g. collaborative filtering, embeddings, ranking models)
- Understanding oftrade-offs in personalisation systems(relevance vs diversity, offline vs online performance, etc.)
- Experience improvingdata quality, analytics, or experimentation frameworks
Benefits
- Employee discount (hello ASOS discount!)
- Employee sample sales
- 25 days paid annual leave + an extra celebration day for a special moment
- Discretionary bonus scheme
- Private medical care scheme
- Flexible benefits allowance – which you can choose to take as extra cash, or use towards other benefits
- Opportunity for personalised learning and in‑the‑moment experiences that enable you to thrive and excel in your role
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