At a Glance
- Tasks: Develop and deploy cutting-edge machine learning and computer vision solutions at Tesco.
- Company: Join Tesco's innovative Data Science team, driving real impact across various business domains.
- Benefits: Enjoy a competitive salary, annual bonus, generous holiday, and private medical insurance.
- Other info: Collaborative culture with continuous learning opportunities and excellent career growth potential.
- Why this job: Make a difference by personalising customer experiences and optimising operations with advanced technology.
- Qualifications: Strong background in computer vision, statistics, and machine learning; coding skills are essential.
The predicted salary is between 50000 - 70000 £ per year.
About The Role
At Tesco, our Data Science team builds scalable solutions to complex business challenges across stores, online, supply chain, marketing and Clubcard. We apply advanced machine learning, computer vision, generative AI, and large language models (LLMs) to personalise customer experiences, optimise operations and drive innovation. We work across several business domains, including customer experience, online, fulfilment, distribution, commodities, store operations and technology. Team members rotate across domains to broaden their expertise and impact. We foster a culture of continuous learning, dedicating 10% of the working week to personal development. Our team benefits from academic partnerships, regular knowledge‑sharing events and a collaborative, inclusive environment that values work‑life balance and professional growth.
What Is In It For You
- Annual bonus scheme of up to 20% of base salary
- Holiday starting at 25 days plus a personal day (plus Bank holidays)
- Private medical insurance
- 26 weeks maternity and adoption leave (after 1 year’s service) at full pay, followed by 13 weeks of Statutory Maternity Pay or Statutory Adoption Pay; we also offer 6 weeks fully paid paternity leave
- Free 24/7 virtual GP service, Employee Assistance Programme (EAP) for you and your family, free access to a range of experts to support your mental wellbeing
You will be responsible for
As a Data Scientist at Tesco, you will develop and deploy machine learning, computer vision and generative AI solutions that directly impact how we serve our customers and run our business. You will be responsible for designing and implementing robust models, experimenting with new approaches, and translating business problems into data science solutions. This includes building prototypes, evaluating model performance both statistically and in terms of business impact, and scaling models into production. You will collaborate closely with Machine Learning Engineers and Software Engineers to ensure solutions are efficient, maintainable, and aligned with Tesco’s technology standards. You will also play a key role in communicating complex ideas clearly to non‑technical stakeholders, contributing to internal knowledge sharing and representing Tesco in the external data science community. Staying current with developments in GenAI and Computer Vision will be essential, as you will help to evaluate and integrate emerging techniques into our data products.
You will need
We are looking for a curious and driven individual with a strong background in computer vision solutions. A mix of statistics and machine learning skills are also welcome. A track record in modifying and designing advanced algorithms and applying them to large real‑world data sets is fundamental. You will translate business problems into data science solutions and communicate your findings clearly to both technical and non‑technical audiences. A scientific mindset, critical thinking, and the ability to ask the right questions are essential. You will be proactive in learning new methods, staying up to date with the latest developments, and contributing to a culture of knowledge sharing and collaboration. A higher degree in a quantitative discipline—such as Mathematics, Computer Science, Engineering, or Physics—is preferred, though equivalent experience in a commercial setting is also valued. Strong coding practices, including version control and testing, are expected.
You Will Also Ideally Have Experience Of The Following
- Deep Learning frameworks: Tensorflow and/or PyTorch
- Object detection models, e.g. Faster‑RCNN, YOLO, EfficientDet, Nvidia models
- Libraries and tools: OpenCV, scikit‑image, SciPy, Pandas, Docker, Git
- Working knowledge of image processing, and video technology
Computer Vision Data Scientist employer: hackajob
At hackajob, we pride ourselves on being an exceptional employer that fosters a culture of innovation and inclusivity. Our diverse team thrives in a high-performance environment where your contributions directly impact our multi-asset platform's success. With ample opportunities for professional growth and a commitment to employee development, joining us means being part of a forward-thinking company that values your expertise and ambition.
StudySmarter Expert Advice🤫
We think this is how you could land Computer Vision Data Scientist
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like hackajob.
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We think you need these skills to ace Computer Vision Data Scientist
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at hackajob, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at hackajob. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at hackajob
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at hackajob!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
