<p><strong>Data Science Manager - Hybrid Working - London/Home</strong></p> <p><strong>What you'll do</strong></p> <p style="margin-left:0cm">As a Data Science Manager and a lead of one of our central Data Science teams, you will play a pivotal role in developing in-house data science solutions that automate decision-making and provide valuable insights across our business. You will lead complex data science development projects, as well as help set the direction for large scale ML transformation programmes. You will line manage and support several data scientists to elevate a high performing team.</p> <p><strong>Why join us</strong></p> <ul> <li><strong>Wide impact.</strong> Deploying models here means improving the experience of millions of customers each day.</li> <li><strong>Range of projects.</strong> Our projects vary from supply chain optimisation for one of the largest logistics networks in the country, predicting which substitution products online shoppers prefer, to helping our instore colleagues keep shelves full.</li> <li><strong>Focus on data science work</strong>.&nbsp;Access to extensive, clean, and well-documented data in our industry-leading platform: spend your time building data science solutions, not cleaning datasets.</li> <li><strong>Time for growth.</strong> 10% of time set aside for learning & personal development</li> <li><strong>Learning and mentoring.&nbsp;</strong>With a team of 50 data scientists, engineers and product managers, there are lots of opportunities to learn from colleagues through knowledge shares, pair programming, and communities of practice.</li> <li><strong>Flexible working.</strong> Our team prioritizes hybrid working, both at home and in our central London Farringdon office.</li> <li><strong>Save on groceries</strong>. 10% discount on products across Sainsburys (Up to 15% for two days each week!)</li> </ul> <p><strong>Key responsibilities of the role</strong></p> <p><strong>Technical leadership</strong></p> <ul> <li>Lead DS development projects as part of cross-functional deliveries with colleagues from our allied teams: Product, Analytics, Science, and Engineering.</li> <li>Own the design and delivery of large scale data science solutions.</li> <li>Drive wider ML transformation programmes to support large business areas to become more data-driven, through coordinating requirements gathering, resource allocation and managing dependencies.</li> <li>Prioritise work across complex delivery programmes and help team members prioritize the right things are the right time.</li> <li>Articulate value statements for Data Science work and produce meaningful measurement methodologies to measure business impact of your solutions.</li> </ul> <p><strong>Line management, Coaching and Team Building</strong></p> <ul> <li>Line manage and develop our data science talent.</li> <li>Conduct code reviews and model our team’s technical standards.</li> <li>Identify opportunities for team upskilling.</li> <li>Take ownership of or lead one of our strategic initiatives: i.e. ML Ops, coding standards, training etc.</li> <li>Pair program with other data scientists.</li> </ul> <p><strong>Essential Criteria</strong></p> <ul> <li>Educated to degree level, preferably&nbsp;within a mathematical, statistical or STEM discipline.</li> <li>A solid track record of individually contributing to value-driving data science projects in a commercial setting.</li> <li>Strong proficiency with Python, SQL, and the data science stack (pandas, NumPy, scikit-learn, etc.)</li> <li>Experience with writing production grade code using Python and SQL, as some of our systems involve real-time inference that affect millions of customer transactions.</li> <li>Experience deploying models in cloud&nbsp;environments&nbsp;(Azure, AWS, GCP).</li> <li>Solid&nbsp;presentation skills and business acumen, you&nbsp;can translate an unstructured business problem into a meaningful data science project.</li> <li>Strong statistical foundation in concepts such as regression, hypothesis testing and experimental design.&nbsp;</li> <li>Highly proficient in Git best practices</li> </ul> <p><strong>Desirable experience</strong></p> <ul> <li>Experience performing as technical lead for ML/AI projects</li> <li>Experience&nbsp;building or&nbsp;maintaining&nbsp;machine learning pipelines and endpoints in Azure ML.</li> <li>Experience delivering value on ML projects in retail&nbsp;(in areas such as price elasticity or differential pricing) or supply chain (demand forecasting, optimisation&nbsp;etc.).</li> <li>Our products&nbsp;often&nbsp;utilize&nbsp;mathematical optimisation, so experience in this area is highly desirable.</li> <li>Experience managing data science teams, especially as part of cross-functional programmes.</li> <li>A background in academic research in a highly numerate field</li> <li>Deep technical knowledge in one or more data science areas: casual ML, GNNs, forecasting, optimisation, operations research, etc.</li> <li>Experience developing agentic based workflows or managing LLMs in production.</li> </ul> <p>&nbsp;</p> <p>#DataAndAnalytics</p> <p>#DataScience</p> <p>#LI-JH1</p>