Must have

• Python

• Polish

• English

Requirements description

• Strong programming skills in Python
• Good knowledge of SQL
• Familiarity with Tensorflow, Keras or similar deep learning frameworks
• Familiarity with modern software development processes such as design documentation, code reviews, CI/CD, testing and project management
• A good comprehension of fundamental machine learning concepts
• Able to document and explain technical concepts for a non-technical audience
• A degree specialising in machine learning, or equivalent experience in a commercial setting
• Prior experience working within an engineering team, whether in a previous role or an internship

What will make you stand out:

• Experience working in Google Cloud Platform
• Knowledge of time series modelling techniques, particularly transformers
• Experience with geospatial data and modelling techniques

Offer description

We are a team of data scientists responsible for the machine learning algorithms within the Logistics & Supply Chain area of Ocado Technology. These algorithms are crucial to ensuring that OSP’s partners are able to run their operations efficiently, covering areas such as demand forecasting, drive time predictions and supplier reliability. We particularly focus on developing state of the art deep learning approaches to these problems.

We own the full machine learning life cycle, which means we both research new algorithmic approaches but also implement and support these in production. For this role we are looking for an individual who has a strong background in machine learning who is also able to write production-quality code, following ML development best practices. Once in the team you can expect to work closely with other team members on both new and established machine learning projects. We are split between London and Kraków, with the line manager for this role being based in London.

Your responsibilities

1. Improving our existing model architectures and adapting them to new challenges and geographies
2. Designing and building new data pipelines to feed more information into our models
3. Supporting our models in production and rolling them out to new retailers
4. Working with product to develop new applications for machine learning in our stream
5. Working with engineering to design the interfaces between our models and the rest of OSP
6. Communicating results to internal and external stakeholders

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