Head of Machine Learning and Data Science

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

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 is handled in the recognition pipeline (face-blurring is an early example), especially given customer security/compliance expectations.
7. Vendor and third-party model evaluation - managing relationships with external model providers and annotation partners as reliance on them grows.

Key responsibilities

Technical direction

• Set and maintain the technical strategy for Winnow's core computer vision pipeline, including food-tracking, waste-stage classification, weight estimation, and food-identity recognition models and beyond.
• Take broader ownership of all ML/AI activities in the company (e.g. production forecasting) once the food recognition accuracy targets are met.
• Act as an advisor on LLM-based product features with a view to owning this area over time.
• Guide model improvement work to maximise performance of the overall system, including fully utilising all hardware capabilities (multiple cameras), and edge and cloud computing capabilities.
• Oversee the creation and upkeep of high-quality datasets and annotation guidelines that the team's models are trained and validated against.
• Drive improvements to ground-truth and label quality, including label aggregation approaches for cases with multiple or conflicting annotations.
• Sponsor and conduct exploratory research into new techniques - for example, using vision-language models as a verification layer on top of existing classifiers - and make informed calls on when to invest further versus park an experiment.
• Set out a clear future-state vision, and a step-by-step plan to deliver it, for stakeholders across the business.
• Own the team's technical workspace, including data repositories, pipelines, toolsets, dashboards, and a combination of cloud and on-prem infrastructure.

Cross-functional partnership

• Work closely with Engineering to build systems that combine software and AI to deliver high accuracy reliably, and ship models into production, including on edge hardware.
• Partner with Product to translate customer and operational needs into a model roadmap, and to explain model performance and limitations in plain terms.
• Represent ML and Data Science in technical reviews and sign-offs for new model releases, RFCs and major pipeline changes.
• Build the business cases for ML investment and work with the Head of Technology on budget allocation.

Team leadership

• Lead, coach, and grow a team of four research and data scientists, setting individual and team goals and supporting their technical and career development.
• Shape the team's hiring plan and lead recruitment for new roles, holding a high bar for every hire.
• Spot and address underperformance early, using clear improvement plans and working with the People team where needed.
• Own the team's quarterly OKRs end to end - shaping objectives, tracking key results, and reporting progress to engineering and product leadership.
• Build a strong team culture around rigorous experimentation, clear documentation, and shared ownership of model quality.
• Manage prioritisation across a busy roadmap of model improvement work, data quality initiatives, and exploratory research.

About you

Experience

• Proven experience leading a machine learning or data science team, including direct people management, ideally within computer vision or a related applied ML domain.
• Strong hands-on background in computer vision - object detection/tracking, classification, or segmentation - and comfort going deep on model architecture and evaluation when needed.
• Experience running ML work through a clear operating rhythm (e.g. OKRs, RFCs, sign-off processes) in a fast-moving product environment.
• Track record of shipping models into real-world production systems, ideally including edge or resource-constrained deployment.
• Experience with data quality and annotation workflows, including label aggregation or multi-annotator agreement techniques.

You might also have

• A PhD or MSc in a relevant field.
• Exposure to using large vision-language or foundation models as auxiliary tools within a production ML pipeline (e.g. verification, labelling assistance).
• Experience working with camera/IoT hardware constraints, or in a company whose product runs on physical devices in the field.
• A background in a data-scarce or noisy-label domain where rigorous experimentation and ground-truth quality make an outsized difference.
• Experience in software engineering practices: automated testing, version control and breaking down larger software systems.
• Experience with graph databases and distributed computing systems (e.g. Spark)

How you work

• You get a lot done and can motivate and lead a team to do the same.
• You lead by example technically, while knowing when to step back and let your team own a problem.
• You're comfortable making prioritisation calls with incomplete information, and can explain the reasoning to both engineers and non-technical stakeholders.
• You care as much about the rigour behind a result (data quality, evaluation methodology) as the result itself.

Our technology stack

You’ll be working with:

• ML frameworks: TensorFlow, PyTorch, Pandas, NumPy
• Python/Linux environment
• MLOps tooling such as MLflow
• Jupyter Notebooks
• Relational and non-relational databases: AWS Redshift, PostgreSQL, vector databases
• Apache Airflow for workflow orchestration
• AWS cloud infrastructure as well as on-premise dedicated hardware
• Git

• Competitive salary
• Pension contribution - up to 6.5% of employer contribution
• Company part-funded private health insurance and eyecare allowance
• Life insurance (3 times base salary)
• Company stock options package
• 2 Wellness hours per month plus a £48 gross monthly wellness allowance
• Employee Assistance Programme – 24/7 helpline for your wellbeing
• 25 days of paid vacation time in addition to national holidays, plus extra vacation time based on length of tenure with Winnow
• Option to purchase up to 5 additional days of annual leave per year
• Learning and development allowance of £300 annually
• Early Finish Fridays – log off at 3pm on a Friday if you've completed your tasks by then
• You will love what you do – waking up every day solving one of the biggest social problems of our generation: food waste