Data Science Manager
Employment Information
Job Description
The big question: why on earth should a Tech professional like you work for a 150-year-old retail chain? Because we’re on a journey. Changing the way we operate. Learning to think nimble. Giving our teams the time and freedom they need to push boundaries. To create amazing systems and technologies. To give our colleagues and our customers even more incredible experiences.
There are thousands of experts to talk to and learn from. We’ve got data from billions of transactions for our teams to play with. Things get built here. They get made here. They hit customers and colleagues quickly. Welcome to the home of Sainsbury’s Data.
More about the role
We have ~80 Analysts and Data Scientists within Marketing focusing on the broad spectrum of customer analytics, and we have 30 million known customers to play with.
This Data Science team is responsible for developing the algorithms and analytics that underpin our bespoke Customer Decisioning Platform - Krang. We do Sainsbury’s most complex modelling, forecasting, tool building, and insights-generation, by blending the latest data science techniques and best-in-class data science tools on an AWS tech stack. We are ‘the brain’ behind an optimised selection of offers, mechanics and discounts used to drive desired customer behaviour. We aim to deliver hyper-personalised messages and offers to customers wherever they interact with our brands. We already deliver over 90 m personalised offers every week to our Digital Nectar customers.
This Data Science Manager role is core to developing and executing our personalisation strategy through a deep understanding of the CRM and Loyalty mechanics and channels available to influence customer behaviour, the application of machine learning techniques to solve complex optimisation problems and the ability to lead a deeply technical team to deliver commercially impactful decisions. They will work hand in hand with Product owners and Engineers using agile workflows to deploy these algorithms in the Krang pipeline moving towards a real time, omnichannel decisioning platform.
Specifically, this Data Science Manager role will be responsible for:
- Managing a team of data scientists and will need to direct, lead and develop that team through coaching and motivating them on a daily basis.
- Have a strong technical skillset to ensure we are delivering the most relevant messages for our customers and lead on our hardest problems
- Taking on the end-to-end responsibility for the technical projects of the team. As part of this, you should challenge the status quo, setting the questions we should answer and challenging our current approaches to continually improve our knowledge.
- You will be an active, participating member of the Analytical team within Retail and Nectar Analytics as well as the broader Data and Analytical community at Sainsbury’s. You should role model what great business behaviour looks like to the more junior members of the team, keeping an eye on the commercial impact of whatever we do whilst thinking how to frame and communicate this to the wider business, and bring stakeholders on the journey with them.
- Working alongside Business and Tech stakeholders to define the Krang roadmap, identifying opportunities to drive both incremental and step changes in our decisioning capability with the aim of increasing the ROI of our marketing investments.
What we’re looking for
- A strong quantitative academic background likely to consist of a university degree (e.g. statistics, mathematics, physics, machine learning, engineering)
- Experience of generating data science solutions by working with customer data to drive business activity is crucial.
- Experienced at managing a team of analysts and/or data scientists, managing their priorities and development with a track record of leading them in a fast-paced environment.
- Extensive programming ability across Python or R and strong ability to use SQL, with a proven experience of analysing data in a corporate environment.
- Experience with PySpark and parallel computing is desirable – we work with very large datasets!
- Experience working at scale and in particular with cloud platforms such as AWS.
- A strong understanding of efficiency and best practice in how code should be written - and an ability to instil this in the wider team.
- Excellent analytical skills and statistical understanding including statistical modelling, probability, recommendation, optimisation problems and other quantitative modelling techniques
- A keen desire to stay abreast of Data Science best practices, emerging technologies and techniques and broader industry trends
- High level understanding of data architecture/governance
- Experience of managing stakeholders at various levels, and good presentation skills (verbal and written). Able to build relationships with people and get things done in a large organisation
- Excellent communicator with the ability to explain complex analytical techniques to nontechnical audiences in a clear and positive manner and the implications of the results.
- Ability to clearly articulate required outcomes and present analytics work in a clear and succinct manner, to colleagues at all levels, across all functions
- Identify opportunities for automation, minimising repetitive tasks and tailoring the analytical and reporting tools available
- Independence to achieve results and work under own guidance and initiative progressing projects and work streams accordingly
- Strong leadership and development of others: and not just for your own immediate team
What we offer
- Colleague discount across the multi-brands – Sainsbury’s, Argos and Habitat
- Holiday allowance
- Bonus scheme
- Pension plan
- Special offers on gym memberships, restaurants, holidays, retail vouchers and more
Flexible working and job share conversations are encouraged. We’re proud to be an equal opportunities employer that champions a diverse and inclusive culture. If you’re reading this, even if you’re not 100% sure you’re there with your experience, we’d still love to hear from you.
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