Job Description

Lead Machine Learning Engineer - Forecasting (3-Month Contract)

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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 accomplished

Lead Machine Learning Engineer

to spearhead innovation in

forecasting and customer behaviour modelling

, enabling more sustainable, data-driven operations.

About the Role

This is a

contract-to-impact

opportunity for an

experienced ML leader or Staff+ individual contributor

who thrives in lean, product-oriented environments. You'll be responsible for

shaping and delivering end-to-end forecasting systems

that 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 someone

autonomous, 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+ years

of experience delivering ML models into production, including

time-series forecasting

or

customer lifecycle modelling

Proven ability to lead complex technical initiatives from ideation through to impact in

high-autonomy environments

Track record of building forecasting systems with measurable commercial or operational value


Advanced Python proficiency and familiarity with libraries like

XGBoost, Prophet, PyTorch, Scikit-learn

Deep knowledge of

MLOps

, reproducibility practices, and scalable experimentation


Experience deploying models with

CI/CD pipelines, containerisation

, and cloud tools (e.g.,

SageMaker, Vertex AI, GCP pipelines

)


Strong data engineering instincts -- including building and optimising

ETL processes

for 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 a

structured proposal

describing 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 a

Lead or Staff-level ML Engineer

ready to shape forecasting strategy and ship high-leverage systems, we'd love to hear from you.