Digital Product at ASOS     

At ASOS, we’re building 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’re hiring a Senior Product Manager to lead Recommendations & Personalisation at ASOS—owning the evolution of our core recommendations platform and key customer experiences. You will be responsible for:

Single-item product recommendations (e.g. “People also bought”)
The core recommendations system powering multiple touchpoints
Outfit recommendations and AI‑driven styling experiences

Your mission is to build a unified personalisation system that enables true 1:1 customer journeys across ASOS—balancing inspiration, relevance, and commercial outcomes. This is a high-impact, platform-leaning role where you will operate across 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’s recommendations ecosystem, spanning retrieval, ranking, and orchestration across surfaces
Drive towards a unified personalisation system across homepage, PLP, PDP, bag, and CRM
Balance short-term commercial optimisation with long-term platform capability building

Deliver high-impact customer experiences

Shape how recommendations show up across the shopping journey—ensuring they are intuitive, inspiring, and on-brand
Partner closely with Product Design to create experiences that enhance discovery without adding friction
Elevate outfit recommendations as a core driver of inspiration and basket building

Lead experimentation and data-driven decision making

Define and run rigorous A/B tests and multi-variant experiments to validate ideas and optimise performance
Establish clear metrics frameworks (conversion, AOV, engagement, profit) and use them to guide prioritisation
Drive a culture of evidence-based decision making, balancing online and offline evaluation insights
Drive improvements in data foundations, observability, and real-time personalisation capabilities

Operate across teams and stakeholders

Work hand-in-hand with ML teams on recommender systems, ranking models, and personalisation logic
Translate complex ML capabilities into clear product decisions and measurable outcomes
Lead across multiple squads, ensuring alignment and coherence across recs experiences and systems
Partner with Commercial, Trading, Marketing, and CX teams to align business goals with customer value
Navigate complex trade-offs between revenue, profitability, availability, and experience quality

Qualifications

About You

Core experience

Proven experience working with recommender systems, ranking, or search-driven products
Strong track record of hands-on experimentation (A/B testing) and data-driven decision making
Experience working closely with machine learning / data science teams to ship customer-facing features
Delivery of consumer-facing products at scale, ideally within ecommerce or marketplaces
Pragmatic builder—able to move quickly from hypothesis to validated outcome
Collaborative leader who can influence without authority across functions

Product craft and leadership

Customer-obsessed and highly curious about how people discover products and make decisions
Ability to operate across multiple teams and initiatives, connecting platform and experience
Strong commercial instincts—able to balance customer value with revenue and profitability
Skilled at turning complex systems into clear strategy, roadmaps, and narratives
Comfortable navigating ambiguity and leading in fast-moving, data-rich environments

Technical and domain understanding

Familiarity with recommender approaches (e.g. collaborative filtering, embeddings, ranking models)
Understanding of trade-offs in personalisation systems (relevance vs diversity, offline vs online performance, etc.)
Experience improving data quality, analytics, or experimentation frameworks

Additional Information

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