Data Scientist - ML for Logistics & Production (Hybrid, London)

Ocado; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

25 days annual leave

Private Medical Insurance

Pension with employer matching up to 7

Cycle to Work Scheme

Shuttle buses

Ocado discounts

Job summary

Ocado is seeking a Data Scientist in London to power its Logistics and Fulfilment ML stack within a hybrid work model. You will own end-to-end data science—from research to deploying production-ready models—supporting demand forecasting, drive-time predictions, and supplier reliability for global partners.

You will work closely with eng, data engineering, and product teams to refine architectures, build robust pipelines, and maintain scalable solutions that impact operations and revenue.

Qualifications

• Proven experience with Python and SQL in a production setting.
• Experience with Google Cloud Platform (GCP).
• 2+ years in data science focused on productionising models, or 2+ years in software/data engineering with ML studies.
• Strong understanding of ML fundamentals and modern software development practices (CI/CD, testing, docs).
• Ability to communicate complex results to non-technical stakeholders.

Responsibilities

• Improve model architectures for operational challenges and scale for global partners.
• Build scalable data pipelines feeding ML models with high-quality data.
• Manage production ML models ensuring availability and performance.
• Collaborate with product and software teams to deploy ML solutions.

Skills

Python

SQL

GCP

Production ML

CI/CD

Documentation

Tools

TensorFlow

Keras

Job description

Ocado is seeking a Data Scientist in London to power its Logistics and Fulfilment ML stack within a hybrid work model. You will own end-to-end data science—from research to deploying production-ready models—supporting demand forecasting, drive-time predictions, and supplier reliability for global partners.

You will work closely with eng, data engineering, and product teams to refine architectures, build robust pipelines, and maintain scalable solutions that impact operations and revenue.