At a Glance
- Tasks: Join Tesco's Data & Analytics team to develop and deploy machine learning solutions.
- Company: Be part of Tesco, a leading retailer focused on data-driven innovation.
- Benefits: Flexible working patterns, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with diverse teams and excellent career advancement opportunities.
- Why this job: Make a real impact by solving complex business problems with cutting-edge technology.
- Qualifications: Degree in engineering, computer science, or related field; experience in machine learning and coding.
The predicted salary is between 50000 - 70000 € per year.
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, architect and engineer data onto these platforms, provide capabilities and tools to the analytics community across Tesco, and develop data products at scale. Our Data Science teams are involved in a broad range of projects, spanning across supply chain, logistics, store and online. These include projects in the areas of Operations Optimisations, Commercial Decision Support (e.g. Forecasting and Range Optimisation), 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 optimisations through to deployment of solutions on the edge, cloud and big-data environments.
As a Machine Learning Engineer, you'll be a significant contributor to the delivery of products in one of Tesco's most strategic technology areas. 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.
Responsibilities
- Participating in group discussions on system design and architecture.
- Working with product teams to communicate and translate needs into technical requirements.
- Working with Data Scientists, Engineers and Product teams across the software lifecycle.
- Delivering high quality code and solutions, bringing solutions into production.
- Performing code reviews to optimise technical performance of data science solutions.
- Supporting production systems, resolving incidents, and performing root cause analysis.
- Continually looking for how we can evolve and improve our technology, processes and practices.
- Sharing knowledge with the wider engineering community.
- Applying SDLC practices to create and release robust software.
You’ll 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.
- Demonstratable experience of building scalable and resilient systems.
- 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.
- 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.
Working patterns
We’re a big business and we can offer a range of diverse full-time & part-time working patterns across our many business areas, which means that we can find something that works for you. We work in a more blended pattern – combining office and remote working. Our offices will continue to be where we connect, collaborate and innovate. If you are applying internally, please speak to the Hiring Manager about how this can work for you – Everyone is welcome at Tesco.
Machine Learning Engineer employer: WeAreTechWomen
At Tesco, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a Machine Learning Engineer, you'll have the opportunity to work on impactful projects that enhance customer experiences while benefiting from flexible working patterns and a commitment to employee growth through continuous learning and development. Our inclusive environment encourages knowledge sharing and teamwork, making Tesco a rewarding place to advance your career in data and analytics.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to current Machine Learning Engineers at Tesco on LinkedIn. Ask them about their experiences and any tips they might have for you. This can give you insider knowledge and maybe even a referral!
✨Tip Number 2
Prepare for technical interviews by brushing up on your coding skills. Use platforms like LeetCode or HackerRank to practice problems related to data structures and algorithms. Being sharp on these will help you stand out during the interview process.
✨Tip Number 3
Showcase your projects! If you've worked on any relevant Machine Learning projects, make sure to highlight them in your discussions. Talk about the challenges you faced and how you overcame them – this shows your problem-solving skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining Tesco's Data & Analytics team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with data science, programming languages, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Showcase Your Projects:Include specific examples of projects where you've applied machine learning or data engineering principles. This could be anything from building scalable systems to optimising code. We love seeing real-world applications of your skills!
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for easy reading and make sure to highlight your key achievements. We appreciate straightforward communication that gets to the heart of your experience.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at Tesco Data & Analytics.
How to prepare for a job interview at WeAreTechWomen
✨Know Your Tech Inside Out
Make sure you brush up on your technical skills, especially in Python and MLOps practices. Be ready to discuss your experience with data structures, algorithms, and any relevant tools like Git or Apache Spark. The more confident you are in your knowledge, the better you'll perform.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled real-world business problems using your technical expertise. Think about specific projects where you contributed to high-impact Data Science solutions and be ready to explain your thought process during the interview.
✨Collaborate and Communicate
Since you'll be working closely with engineers, data scientists, and product teams, practice articulating your ideas clearly. Be prepared to discuss how you translate technical requirements into actionable tasks and how you’ve participated in group discussions on system design.
✨Stay Curious and Eager to Learn
Demonstrate your passion for continuous improvement by discussing how you keep up with emerging technologies and practices in the field. Share any experiences where you’ve sought out new knowledge or tools to enhance your work, showing that you're committed to evolving alongside the industry.
