Senior Product Manager (Recommendations)

Digital Product at ASOS 

At ASOS were 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 customeranticipating needs surfacing inspiration and removing friction at every step. 

Personalised recommendations sit at the heart of this visiondriving meaningful commercial impact while helping customers discover products and outfits they genuinely love. 

About the Role 

Were hiring a Senior Product Manager to lead Recommendations & Personalisation at ASOSowning 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 AIdriven styling experiences 

Your mission is to build a unified personalisation system that enables true 1:1 customer journeys across ASOSbalancing 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 Youll Do 

Own recommendations strategy and platform direction 

• Define and lead the roadmap for ASOSs 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 journeyensuring 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 builderable 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 instinctsable 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 

Remote Work :

No

Employment Type :

Full-time