Sobre esta vaga de Head of Machine Learning and Data Science na Winnow

Winnow · Presencial · London, England, United Kingdom

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:

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.
ML operations - designing and building highly automated ML pipelines, reproducible processes, and an infrastructure designed for continuous improvement.
Performance measurement - defining the metrics used to objectively measure the performance of the ML systems, diagnosing and troubleshooting issues.
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.
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.
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.
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

Benefits

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

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Candidatar-se à Winnow

Sobre a Winnow

Food waste is a $1 trillion problem – costing the world over 1% of global GDP. We’re dead set on solving the problem and looking for people to help us achieve our mission. We, at Winnow, believe that food is far too valuable to waste, and that technology can transform the way we produce food. Our team is made of people who all share a passion for food and technology.

Winnow was founded in London in 2013 to help the hospitality industry prevent food waste through internet of things tools in the kitchen. We have worked with hundreds of sites and are operating in over 70 countries around the world supported by our offices in London, Dubai, Shanghai, Singapore, Romania and North America. We are a rapidly growing company with a strong base of clients who are rolling out our system globally. We have blue-chip customers including Accor Hotels, IKEA, IHG, Marriott, Compass Group and many others.

Winnow’s clients on average reduce waste by over 50% by value and sustain savings. Winnow has now worked with hundreds of sites to reduce food waste, including hotels, universities and schools, staff restaurants, event/hospitality kitchens, buffets, pubs, and high street restaurants. Where the system is permanently adopted, pre-consumer waste value is reduced by 50% - 70% with no detrimental impact to the perceived quality or value of the offer to their customers. This represents a typical improvement of food cost savings of 3% to 8%, commonly a 40%+ increase in profitability for operations.

As the global leader in addressing food waste, we are committed to continue pushing the envelope on what technology can do to solve this problem. Winnow Vision, our new artificial intelligence-based technology, is trained to automatically track all food waste thrown away. It has won awards at the World Economic Forum and has received tremendous enthusiasm from our clients and the industry. You can read more about it on our website and this article in Forbes.

Other recent accolades saw Winnow awarded a winner of Impact 50's most impactful companies to work for. You can read more about it here.

We are passionate about living our values and place them at the centre of everything we do. We are excited about like minded talent who share these values, joining us in our mission:

Equal parts head and heart. We’re both passionate and measured. We carefully balance the need for quick solutions and pragmatism with the ability to step back, take in the bigger picture and build for the long term.

Bravely honest. With each other, that means we’re a transparent organisation where healthy, respectful debate is encouraged. With our customers, we challenge them if we don’t think they’re achieving their goals, whether they be environmental or financial.

People of action. Done is better than perfect, and we learn by boldly doing then rapidly improving. We’re breaking new ground, so we know things might go wrong. But we judge ourselves and each other on our reaction and our resilience.

Bound by food. We’re a diverse bunch, but our belief in the value of food is the common thread in everything we do. With each other, we celebrate through our love and respect for food. With our customers, it means we work hard to develop creative tools to make it easy for chefs to value food.

Hungry and humble. Our product is revolutionary, our people are impressive, and we’re hungry for change. But, we’re just the catalyst for a bigger movement. We stay humble regardless of our success, and make chefs the heroes in this journey.

People and planet positive. We’re caretakers of the planet, helping to preserve and support it for now and the future. Our work already minimises the impact that the hospitality industry has on the planet, and we’re also committed to actively reducing our own footprint while doing so. We’re leaving the planet and its people better off than we found them.

This is an opportunity to join an exciting organisation and help us propel our growth at what are truly the most exciting and dynamic points in time in our business. You will work alongside a driven team who are motivated by building an exciting business and leaving the world a better place than we found it.

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