<p><strong>Senior Data Scientist - Technical Specialist - Hybrid Working - London/Home</strong></p> <p><strong>Why join us</strong></p> <p>In Customer Data Science (part of the Data Science Hub), we build the systems behind personalised customer decisioning – from customer segmentations to offer optimisation. We impact millions of customers, bringing them value and earning their loyalty.</p> <p>This team already personalises all of the offers you see in the Nectar app, but now we’re growing in ambition and scale. We aim to personalise everything that matters – offers, online recommendations, digital experiences, communications. And we aim to achieve this using our incredible data asset, our rich customer understanding, and state of the art machine learning and AI.</p> <p>This is a new and unique role. You’ll operate as a deep <strong>technical specialist</strong>, floating across two teams in Customer Data Science (and more as we grow). You’re not here to manage a team; you’re here to solve the hardest problems, raise the technical bar, and ensure that we are building robust, production-grade products.</p> <p>This isn’t a consultancy role – you’ll drop into ambiguous, high-impact problems, and solve them yourself. But you’ll also define the patterns we need – how we build models, how we productionise them, how we scale without breaking.</p> <p><strong>What you’ll do</strong></p> <p>Solve the hard problems&nbsp;</p> <ul> <li>Take on ambiguous, high-value problems across customer understanding, personalisation, optimisation and decisioning.</li> <li>Advance techniques used across the team (uplift modelling, optimisation, causal inference, recommendations, real-time decisioning).</li> </ul> <p>Set us up for success</p> <ul> <li>Define and embed best practice across modelling, experimentation, deployment, and model lifecycle management.</li> <li>Drive consistency in how we structure codebases, version control, testing and reproducibility.</li> <li>Coach through doing – pairing on code, demonstrating standards, piloting your ideas.</li> <li>Represent our needs to the Platform team, ensuring we get the ML Ops platform we need.</li> <li>Work with the ML Engineering team to shape how we architect our end-to-end solution – allowing us to reach more customers more quickly, iterate more rapidly, make our data products more available, and reduce friction and hand-offs.</li> </ul> <p>Be an enthusiastic member of our community</p> <ul> <li>Become the recognised data science technical expert in Customer Data Science, bringing new ideas for future approaches, including state of the art techniques where appropriate.</li> <li>Understand how our business really works, including by supporting our stores during peak trading periods.</li> <li>Actively contribute to our vibrant Data&nbsp;and Analytics&nbsp;community of over 800 colleagues, providing a view on new techniques and approaches that can drive positive change in wider teams.</li> </ul> <p><strong>Who you are</strong></p> <p>We’re looking for a highly motivated self-starter – someone who fixes problems and creates value without micromanagement. You need to thrive in an ambiguous role, and know how to balance our need for technical rigour against our need to deliver commercial and customer value.</p> <p>Value delivery</p> <ul> <li>Deep experience in Data Science roles, with evidence of solving complex problems end-to-end.</li> <li>A record of building personalisation systems which run in production, and an understanding of the value these generated.</li> <li>Experience working in an Agile way, and ability to understand the trade-off between immediate value, future value, and technical rigour.</li> <li>A strong ability to communicate ideas to audiences of varying technical background and seniority.</li> </ul> <p>Data Science expertise</p> <ul> <li>Strong grounding in statistical modelling and machine learning, such as predictive modelling at scale, unsupervised learning, causal inference, experimentation, optimisation and decisioning</li> <li>Strong understanding of the “how” behind the algorithm; ability to select the right technique for a given objective and avoid pitfalls.</li> <li>Curiosity, scepticism and attention to detail regarding data quality, samples, bias and ethics.&nbsp;</li> </ul> <p>Production-grade design</p> <ul> <li>Extensive programming ability across Python and strong ability to use SQL, with a proven experience of developing complex solutions in a corporate environment.&nbsp;</li> <li>Solid understanding of version control, dependency management, CI/CD and automated testing, and batch and near real-time processing.</li> <li>Practical experience working on cloud-based ML Ops platforms.</li> <li>Ability to see the big picture – from data ingestion to decisioning output.</li> </ul> <p>Development and coaching</p> <ul> <li>A strong awareness and understanding of technology trends and direction in Data Science, analytics and AI.</li> <li>Ability to support in the development, training and mentoring of others.</li> </ul> <p><strong>Essential Criteria</strong></p> <ul> <li>Extensive Data Science background</li> <li>A record of building systems which run in production</li> <li>A strong ability to communicate ideas to audiences of varying technical background and seniority</li> <li>Strong grounding in statistical modelling and machine learning, including predictive modelling and recommendations/decisioning</li> <li>Extensive programming ability across Python and strong ability to use SQL</li> <li>Solid understanding of working with production codebases, such as version control, CI/CD, batch and real time processing</li> <li>Strong understanding of ML Ops and working in cloud environment.</li> </ul> <p>#DataAndAnalytics</p> <p>#DataScience</p> <p>#LI-JH1</p>