Benjamin Lehne – Data Science Manager at Tesco
Tell us a little about yourself. What do you do? What’s your job role?
I’m a Data Science Manager within the Data Science & Analytics practice. My team develops and maintains the Machine Learning models that power personalisation on the Tesco website and it’s my job to coordinate the work across software engineers, data scientists and business stakeholders
What inspired you to pursue a career as a Data Science Manager and how did you get started?
I spent the first ten years of my working life doing genomics research in academia. During that time, I really enjoyed analysing large datasets, which felt a lot like solving puzzles. So that led very naturally to an industry career in Data Science.
What’s been your biggest achievement while working at Tesco? And how did it come about?
Almost all models my team develops are assessed using AB-testing. Personally, I find this makes the job very fun as it adds an element of gamification. One of our recent models even generated substantial extra revenue, which was a great achievement.
What challenges have you faced in your career and how have you overcome them?
Data Science requires a fair amount of continuous learning. I used to do some of that in my own time, but at Tesco we get dedicated time at work for learning and personal development, which I think is fantastic. And I’ve been able to have a better work/life balance because of it.
How do you stay motivated and continue to grow in your career?
Personally, I’ve never really struggled with motivation. I really enjoy the work I do – it still mostly feels like I’m just solving puzzles.
What’s the best thing about working for Tesco?
It’s a competent, friendly and collaborative team working on interesting problems.
What’s one piece of advice you’d like to share with others who may be at a similar stage in their career?
Focus less on technical skills and more on soft skills and business understanding.
How does Tesco encourage and support diversity and inclusivity across the tech teams?
As well as regular D&I training events for anyone involved in recruitment, we’ve also tried to remove bias from our recruitment process as much as possible. For example, technical exercises are scored “blind”, i.e., the assessor doesn’t know who the candidate is. And each candidate is assessed by up to eight different members of the team. In addition to this, we participate and support specific outreach events and conferences.