Software Engineer (Machine Learning - MLOps)

Opis oferty

About the role

We are looking for a Machine Learning Engineer, to join our growing Data Science Engineering team. You’ll work with other engineers, data scientists, product managers, systems engineers, and analytics professionals to help deliver valuable and innovative outcomes for our customers. You’ll work within and across our Engineering and Data Science teams, delivering scalable products that improve how we serve our customers and run our operations.

This role would suit someone with previous experience working as an ML Engineer / Data Science Engineer.

About the Team:

Within Tesco Data & Analytics, we help our customers and the communities where we operate get the most value from data. We build and run Tesco’s data platforms, we architect and engineer data onto these platforms, provide capabilities and tools to the analytics community across Tesco, develop data products at scale and maintain existing applications.

Our Data Science team are involved in a broad range of projects, spanning across supply chain, logistics, store and online. These include projects in the areas of Operations Optimizations, Commercial Decision Support (e.g. Forecasting, Range Optimization, Supply Chain support), Online (e.g. Search and Recommendation) and Intelligent Edge (e.g. Computer Vision). Our Machine Learning Engineers work alongside our data scientists, helping with everything from development of tools and platforms, code optimization through to deployment of solutions on the edge, cloud and big-data environments.

What is in it for you

Wymagania

You come from either an Engineering or Data Science background, with a good understanding of the Data Science Toolkit (Programming, Machine Learning, MLOps etc) and bringing data science solutions into production. You therefore tick the majority of the following points:

Key Requirements:

• A higher degree in engineering, computer science, maths or science.
• Customer focus with the right balance between outcome delivery and technical excellence.The ability to apply technical skills and know-how to solving real world business problems.
• Demonstrable experience of building MLOps systems according to the best market standards.
• Commercial experience contributing to the success of high impact Data Science projects within complex organisations.
• Awareness of emerging MLOps practices and tooling would be an advantage e.g. feature stores and model lifecycle management.
• knowledge of ML workflow/orchestration platform like Airflow
• An analytical mind set and the ability to tackle specific business problems.
• Experience with different programming languages and a good grasp of at least one language. The ideal candidate is fluent in Python.
• Use of version control (Git) and related software lifecycle tooling.
• Experience with tooling for monitoring, logging and alerting e.g. Splunk or Grafana.
• Understanding of common data structures and algorithms.
• Experience working with open-source Data-Science environments.
• Knowledge of open source big-data technologies such as Apache Spark.
• Experience building solutions that run in the cloud, ideally Azure.
• Experience with software development methodologies including Scrum & Kanban.
• A background or strong understanding of the retail sector, logistics and/or ecommerce would be advantageous but is not required.
  

Unsure if you fit all the criteria? Apply and give us the chance to evaluate your potential – you could be the perfect fit!

Zakres obowiązków

• Participating in group discussions on system design and architecture
• Working with product teams to communicate and translate needs into technical requirements
• Working alongside our Data Scientists, Software Engineers and Product teams across the software lifecycle
• Delivering high quality code and solutions, bringing solutions into production
• Performing code reviews to optimize technical performance of data science solutions.
• Supporting production systems, resolving incidents, and performing root cause analysis
• Look for how we can evolve and improve our technology, processes and practices
• Sharing knowledge with the immediate engineering team
• Collaborate with other teams on more complex initiatives via working groups
• Applying SDLC practices to create and release robust software