At Tesco, our Data Science team focuses on modelling complex business problems and deploying data products at scale. Our work spans across multiple areas including physical stores, online, supply chain, marketing and Clubcard, where we encourage rotation amongst our Data Scientists so they can gain expertise in different subjects.
We work on several domains and problem types: online, pricing, security, fulfilment, distribution, property, IoT and computer vision are just some. Our team members spend 10% of their week on learning and personal development. Multiple academic collaborations enrich the team expertise; knowledge sharing events are regular. Furthermore, we have a great work-life balance, team days and relaxed but engaging culture.
At Tesco, we are committed to providing the best for you.
As a result, our colleagues enjoy a unique, differentiated, market- competitive reward package, based on the current industry practices, for all the work they put into serving our customers, communities and planet a little better every day.
Our Tesco Rewards framework consists of pillars - Fixed Pay, Incentives, and Benefits.
Total Rewards offered at Tesco is determined by four principles -simple, fair, competitive, and sustainable.
Salary - Your fixed pay is the guaranteed pay as per your contract of employment.
Leave & Time-off - Colleagues are entitled to 30 days of leave (18 days of Earned Leave, 12 days of Casual/Sick Leave) and 10 national and festival holidays, as per the company’s policy.
Making Retirement Tension-FreeSalary - In addition to Statutory retirement beneets, Tesco enables colleagues to participate in voluntary programmes like NPS and VPF.
Health is Wealth - Tesco promotes programmes that support a culture of health and wellness including insurance for colleagues and their family. Our medical insurance provides coverage for dependents including parents or in-laws.
Mental Wellbeing - We offer mental health support through self-help tools, community groups, ally networks, face-to-face counselling, and more for both colleagues and dependents.
Financial Wellbeing - Through our financial literacy partner, we offer one-to-one financial coaching at discounted rates, as well as salary advances on earned wages upon request.
Save As You Earn (SAYE) - Our SAYE programme allows colleagues to transition from being employees to Tesco shareholders through a structured 3-year savings plan.
Physical Wellbeing - Our green campus promotes physical wellbeing with facilities that include a cricket pitch, football field, badminton and volleyball courts, along with indoor games, encouraging a healthier lifestyle.
This is a hands-on position where you will need to leverage your analytical mindset to find solutions to complex problems. As a Data Scientist, you will need to understand difficult business problems and prototype solutions with minimal support. Apply, modify and design algorithms and mathematical models to solve business problems on top of big data architectures (Hadoop, Spark) is a core component of the role. Our data scientists will need to be able to validate, document and present the modeling process and performances, as well as communicate complex solutions in a clear, understandable way to non-experts. Data Scientists are also responsible for promoting data science across Tesco and promote Tesco across the external Data Science community.
We are looking for ambitious individuals with a mix of statistics, programming, and machine learning skills. The role requires that you have an extensive background in machine learning and data mining. A track record in modifying and designing advanced algorithms and applying them to large data sets is essential.
An ideal candidate will have a scientific mentality with the ability to ask the right questions, as well as answer them. A strong numerical higher degree in a mathematical, scientific, engineering or computer science discipline is preferable, as well as a solid understanding of mathematics and statistical principles.
Experience in one or more of the following fields is required: predictive modelling, operational research, deep learning, and time series modelling. Finally, strong programming experience in Python.
