Head of Machine Learning and Data Science (Hiring Immediately)

About us

Food waste costs the world over $1 trillion a year. At Winnow, we're on a mission to change that — bringing AI-powered technology into hospitality kitchens to help eliminate waste at scale, with an ambition to save our clients $1 billion annually.

We started in London in 2013 with a simple belief: technology could make kitchens smarter and food systems more sustainable. Today that belief has grown into a global team spanning the UK, Romania, Dubai, Shanghai, Singapore, Malaysia, North America, and Mexico, working across 90+ countries and 3,500+ sites.

We're proud of the results we help our clients achieve. Names like IKEA, Hilton, Accor, Marriott, IHG, Compass Group, Iberostar, and Mandarin Oriental trust us to cut their food waste by over 50% on average — and when our technology is fully adopted, it reduces pre-consumer waste by 50–70% and delivers food cost savings of 2–8%.

We're building something that matters, and the people who thrive here feel that every day. We're proud to have been named one of The Sunday Times Best Places to Work and ranked #4 in the FoodTech 500 (2025) — recognition of both our innovation and our commitment to building a purpose-led company where people can make a real difference.

Our values

Our values sit at the centre of all that we do. If they resonate with you, we'd love to hear from you and for you to help us on our mission.

• People of action - done is better than perfect. We move fast, learn faster
• Equal parts head and heart - we balance passion with pragmatism
• Hungry and humble - we're ambitious about our mission, not our egos
• Bravely honest - we say what we think, respectfully, with colleagues and customers alike
• Bound by food - it's what unites us, and it's what we're fighting for
• People and planet positive - we're leaving both better than we found them

Requirements

About the role

The Machine Learning and Data Science team builds and maintains the computer vision models at the heart of Winnow's product: systems that watch a kitchen bin and turn what they see into accurate, trustworthy waste data. We're looking for a Head of Machine Learning and Data Science to take full ownership of one of the most technically challenging areas in the company. You’ll lead the team, set the technical direction, and make sure our models keep getting better as Winnow scales into new sites, new cameras, and new food environments.

Your first priority is clear: take food recognition to the accuracy our customers need, which means bringing rigour to data quality, labelling, evaluation and ML operations. From there, your remit extends to production forecasting, and over time to shaping how we use AI more broadly across the product, including LLM-based features.

This is a hands-on leadership role for someone who wants deep technical ownership and direct impact, rather than a large organisation. Reporting to the Head of Technology, you’ll be a key part of the technical leadership of Winnow. You’ll make the case for where we invest in ML, and be the bridge between ML and the rest of the business - translating model performance into terms that product, engineering and customer-facing teams can act on.

As the function matures, we expect this role's remit to grow beyond the initial scope focused on food recognition. That includes:

1. Strategic ownership - develop and execute a long-term vision of creating a state-of-the-art AI/ML ecosystem. Influence how AI and ML are used in Winnow’s products and how we develop best-in-class models and techniques.
2. ML operations - designing and building highly automated ML pipelines, reproducible processes, and an infrastructure designed for continuous improvement.
3. Performance measurement - defining the metrics used to objectively measure the performance of the ML systems, diagnosing and troubleshooting issues.
4. Annotation and labelling processes - owning the tooling and processes behind how training data gets labelled to ensure that we have what we need to drive high-quality AI systems.
5. Edge/device and camera hardware input - technical input into camera and edge-compute decisions as new hardware generations come online, not just building models to fit existing constraints.
6. Privacy and responsible AI - broader ownership of how personal data]]>