Lead Machine Learning Engineer – Forecasting (3-Month Contract)
Location:Hybrid (minimum 1 day/week in our Vauxhall, London office)
Contract Type:Full-time, Fixed-Term (3 months)
Salary:Competitive, based on experience
Eligibility:UK-based applicants only
About Oddbox
Oddbox is on a mission to fight food waste and transform the food system through our fruit and veg subscription service. To date, we've rescued over 50 million kilograms of produce that would have otherwise gone to waste — but we’re just getting started.
As we scale our environmental impact, we’re investing in smarter technology and forward-looking ML capabilities. We're now seeking an accomplishedLead Machine Learning Engineerto spearhead innovation inforecasting and customer behaviour modelling, enabling more sustainable, data-driven operations.
About the Role
This is acontract-to-impactopportunity for anexperienced ML leader or Staff+ individual contributorwho thrives in lean, product-oriented environments. You’ll be responsible forshaping and delivering end-to-end forecasting systemsthat influence core supply chain and customer engagement decisions.
You will:
Lead high-stakes forecasting projects — from ambiguous ideas to productionised ML systems
Architect and deploy solutions in collaboration with Product, Engineering, and Ops
Ensure data and model pipelines are scalable, reproducible, and value-driven
Create measurable business impact within a focused 3-month engagement, with potential for longer-term collaboration
We’re looking for someoneautonomous, decisive, and outcome-obsessed— capable of steering technical decisions, influencing stakeholders, and shipping ML systems that matter.
Responsibilities
Forecasting Impact: Design, develop, and deploy forecasting models that optimise supply, reduce food waste, and improve customer outcomes
Technical Leadership: Define modelling approaches, data strategies, and validation pipelines in a cross-functional context
Strategic Execution: Rapidly prioritise, structure, and execute on projects with evolving requirements and commercial pressure
System Architecture: Build production-grade, containerised models using modern MLOps practices — integrating with cloud pipelines and multi-source data
Data-Driven Culture: Drive experimentation, model performance monitoring, and business integration of ML outputs
What We’re Looking For
8+ yearsof experience delivering ML models into production, includingtime-series forecastingorcustomer lifecycle modelling
Proven ability to lead complex technical initiatives from ideation through to impact inhigh-autonomy environments
Track record of building forecasting systems with measurable commercial or operational value
Advanced Python proficiency and familiarity with libraries likeXGBoost, Prophet, PyTorch, Scikit-learn
Deep knowledge ofMLOps, reproducibility practices, and scalable experimentation
Experience deploying models withCI/CD pipelines, containerisation, and cloud tools (e.g.,SageMaker, Vertex AI, GCP pipelines)
Strong data engineering instincts — including building and optimisingETL processesfor large, structured and unstructured datasets
Exceptional collaboration skills with the ability to influence both technical and non-technical stakeholders
Our Hiring Process
We value efficiency and depth. Our process is designed to evaluate how you think, architect, and lead — not just what you can code.
Introductory Call (15 minutes)
A short conversation to align expectations, discuss the scope, and answer your initial questions.
Strategic Forecasting Deep Dive (Take-Home Proposal)
You’ll receive a realistic brief outlining a forecasting challenge relevant to our domain. We’ll ask you to prepare astructured proposaldescribing how you would approach the problem — including your assumptions, modelling choices, data needs, and delivery roadmap.
We’re not testing syntax — we’re evaluating your ability to frame problems, communicate clearly, and drive outcomes.
Final Interview (90 minutes)
A collaborative session with our Data and Product teams focused on your approach, technical architecture, decision-making, and stakeholder alignment.
Final Note
This is a rare opportunity to make an immediate, mission-driven impact at a company that values experimentation, autonomy, and sustainability. If you're aLead or Staff-level ML Engineerready to shape forecasting strategy and ship high-leverage systems, we’d love to hear from you.
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