About Lyst
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 & 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 & discovery experience which offers inspiration, fulfilment, and personalisation. We believe that fashion is amazing but shopping for fashion often isn't, and 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
We're looking for a commercially minded and technically strong Machine Learning Engineer to join our Product Listings team—focused on unlocking smarter, more scalable advertising through automation and optimisation.
This is a hybrid role combining deep data science expertise with production-grade ML engineering. You’ll be a driving force behind our performance marketing automation: designing experiments, building predictive models, and deploying them at scale to help optimise product bidding and maximise profit across platforms like Google Shopping.
Expect to work across the full ML lifecycle—from exploring large, messy datasets and engineering features, to evaluating models offline and running experiments to validate impact. At the same time, you’ll build, deploy and monitor models in production: setting up retraining workflows, pipeline orchestration, and performance alerts. You will be supported in this by your tech lead and colleagues from the wider Data Science chapter at Lyst.
We work mainly in Python using all the standard ML toolkits and frameworks (e.g. SKLearn, Tensorflow, Pytorch), and run our ML code in the AWS environment using Sagemaker where possible. We have a strong preference for clean, documented, well tested and reviewed code and have tooling and a culture to support this.
This is a hands-on, high-impact role that blends research, experimentation and engineering, all tied to clear business outcomes. You'll collaborate closely with marketers, data analysts and engineers, and play a key part in shaping the future of how we scale our advertising.
Responsibilities
- Productionise Machine Learning models into robust, maintainable systems—including retraining pipelines, monitoring, and performance alerting
- Build and evaluate ML models that support smarter paid marketing—e.g. product-level ROAS prediction, lifecycle-aware bidding, and grouping optimisation
- Explore large-scale product and performance datasets to uncover insights, test hypotheses, and identify features that drive business value
- Work on all parts of the data science workflow: from data cleaning and exploratory analysis to offline model benchmarking and validation
- Collaborate with marketers to reduce manual overhead in campaign management while improving ad efficiency and profitability
- Improve our ad feed infrastructure to ensure product attributes, pricing and availability are optimised for campaign performance
- Contribute as needed to product data initiatives (e.g. classification, deduplication), especially where they impact ad quality or model input
- 3+ years of experience building and deploying ML models in production
- Strong Python and SQL skills, with a solid understanding of data wrangling, feature engineering and model evaluation
- Deep understanding of the data science process—comfortable with exploratory analysis, statistical testing, and model comparison techniques
- Experience with structured prediction problems (e.g. regression, classification, ranking) using real-world, often messy data
- Familiarity with advertising systems (Google Shopping, PMAX, etc.) and marketing metrics like ROAS, profit, and conversion is a strong advantage
- Experience productionising ML models—setting up training pipelines, versioning, monitoring and retraining
- Our Ways of Working:We all come into the office onTuesdaysandThursdays, with the option to work remotely or come into the office on the other days. We believe that in person collaboration and community spirit is super important, which is why we spend some of our time in the office and some of our time at home.
- Time Off:In addition to the 8 statutory bank holidays, you will receive 29 holidays per year. Lyst’s holiday year runs from 1 April to 31 March.
- Competitive Family Leave Package:This includes Enhanced Family Leave for those eligible, paid Time off for Dependents and Support for Fertility Treatment & Loss.
- Clothing Benefit:We want you to enjoy using the Lyst app and site as much as our customers, so we provide you with a clothing allowance to use on Lyst every year. This starts at £250 when you join and increases up to £1,000 with your length of service.
- Private Healthcare:Our healthcare provider is Vitality. Your health is important to us which is why we offer all employees a comprehensive healthcare scheme from the day you start.
- Training Allowance:We’re big on continuous learning and growth, so all employees are currently entitled to an annual training allowance of £1,000. This can be used to attend conferences, industry events, training courses and to purchase resources.
- Pension Scheme:Our pension provider is The People’s Pension. We offer a minimum employee contribution of 5% and 3% employer contribution.
- Eye Tests and Vouchers:Employees can make a saving on their eye test and glasses through our chosen provider. You’ll receive a free eye test every year and a discount towards glasses.
- Cycle-to-Work Scheme:Lyst will purchase a bicycle from your chosen retailer, you will then receive a voucher to pick up your bicycle from them.
- Transport Season Ticket Loan:Employees can apply for an interest free season ticket loan to support your travel to work.
- Social Events:Frequent company wide social events including Christmas & summer parties, sports days, themed drinks, quizzes, cook alongs, as well as smaller team socials. We also have plenty of interest based groups such as football, running club, book club, culinary and more.
We want to build a world where fashion works for everyone, and we want teams that are just as inclusive. Diversity and inclusion is an integral part of our culture at Lyst. We recognise and celebrate the value and impact diversity brings to our company and are committed to ensuring this is a consistent focus, for which we are held to account. We are committed to treating all applicants fairly and equally, and encourage candidates from all backgrounds to apply for this role. We are happy to talk about flexible working arrangements.
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At Lyst we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
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