Technical Product Manager, Product Data & Revenue Platforms (Hiring Immediately)

Lyst is a global fashion shopping platform founded in London in 2010 and catering to over 160M shoppers per year. We offer our customers the largest assortment of premium and luxury fashion products in one place, curating pieces from 27,000 of the world's leading brands and stores. In 2025, Lyst joined Zozo, operators of Zozotown, the leading fashion e-commerce platform in Japan. This partnership marks a bold new era for Lyst, as we accelerate our vision and work together to transform the future of fashion shopping through AI and technology.

At Lyst, we obsess over the customer, providing a search and discovery experience which offers inspiration, fulfilment, and personalisation. We believe that fashion is amazing but shopping for fashion often isn't, and we use our technology, data and creativity to bring more joy, greater choice and fewer fails. Our mission is to help fashion shoppers make better decisions and help fashion partners find better audiences as the category-leading destination for every fashion shopper.

Role

Product data, and the systems used to transform and enrich it, sit at the heart of how Lyst serves customers and grows revenue. We are looking for a technically strong Product Manager to lead the direction and delivery of the platforms that support our affiliate operations, paid marketing and product-data enrichment.

You will work across two connected missions. The first is helping our commercial teams launch and improve high-value capabilities such as channel integrations, product feeds, conversion and cost data, attribution, reporting and ROAS optimisation. The second is improving the quality, reliability and discoverability of Lyst's product catalogue through internal tooling, product data enrichment, merging and deduplication, leveraging our partnership with our Data Science team in New Zealand.

This is not a conventional consumer-facing Product Manager role. Many requests will arrive with a clear commercial goal and a proposed solution. Your job is to use judgement: move quickly when the problem and solution are well understood; challenge, simplify or productise the request when that will create more value; and make the trade-offs visible when BAU, reliability, technical debt and roadmap work compete for the same capacity.

What you'll do

• Own the joined-up roadmap for the Affiliate Operations team, Commercial Enablement and Product-Data Enrichment, with clear outcomes, sequencing and measures of success.
• Turn commercial priorities into well-shaped technical work, partnering with stakeholders who may already have a strong view of the solution.
• Decide when a request should be delivered directly, when assumptions need testing, and when repeated one-off work should become a reusable platform capability.
• Partner with the Engineering Lead and team to clarify scope, dependencies and acceptance criteria, maintain a healthy backlog and improve delivery predictability.
• Lead planning and communication across Paid Marketing, Partnerships, Operations, Data Science and Product & Technology, making trade-offs and capacity constraints explicit.
• Act as the primary Lyst product contact for the NZ-based Data Science team, aligning roadmaps and turning research outputs into secure, observable and supportable production capabilities.
• Define and track outcomes across commercial performance, data quality, platform reliability and operational efficiency.

What you'll work on

• Paid-channel enablement: product feeds (e.g. for Google Merchant Center), conversion APIs, cost APIs, attribution, reporting and integrations for channels such as Pinterest and Reddit.
• Reusable commercial capabilities: common services, data contracts and self-service tooling that reduce duplicated work across channels and campaigns.
• ROAS and optimisation: productionising bidding, measurement and optimisation models with the controls, monitoring and feedback loops needed to operate them safely.
• Partner and affiliate platforms: partner feed ingestion and validation, merchant tooling and the services that connect catalogue and commercial workflows.
• Product-data quality: enrichment, merging, deduplication, entity resolution, taxonomy and measurable improvements to catalogue completeness and coherence.
• ZOZO collaboration: joint milestones, experiments, data contracts, technical workshops and clear handovers across time zones.
• Platform health: SLAs, observability, runbooks, incident follow-up, peak readiness, BAU and a pragmatic technical roadmap across services such as Minion, Plasma, Merchant Uploader and Affiliate Service.
• Portfolio management: an effective intake process, tran]]>