Data Science Manager

Leeds
2 months ago
Applications closed

Related Jobs

View all jobs

Data Science Manager

Data Science Manager – Gen/AI & ML Projects - Bristol

Data Science Manager

Data Science Manager Gen/AI & ML Projects - Bristol

Data Science Manager

Data Science Manager

You'll oversee a number of Data Science teams, owning the delivery of high-quality solutions, while being able to remain hands on and get into the detail. As our Data Science Manager, you'll collaborate with senior business stakeholders and data teams, promote and help evolve our ways of working, and ensure our teams build out advanced and measurable analytics and machine learning solutions that realise demonstrable value to our business.

As our Data Science Manager, you'll have access to a wide range of benefits including:

Hybrid working (we're in the office 3 days per week)
Manager's bonus
Annual pay reviewsWhat you'll be doing:

With demonstrable experience in managing Data Science teams and taking projects from concept to production, you'll motivate the Data Science teams to deliver high-quality Data Science, Machine Learning and AI solutions appropriate to the challenges on the agreed Roadmap. You'll also:

Develop a deep understanding of the business area the teams are working in, and own the solution designs the team proposes to key questions in those areas.

Ensure adherence to the DS process and ways of working, understand each pod's capacity and being sure they are appropriately utilised.

Have an expectation of remaining hands on up to 40% of the time.

Provide regular, clear communication to the Data Science Management Team on the status of delivery initiatives in the pods, including risks and issues impacting delivery.

Work with our Product Owners to prioritise DS tasks that are committed and unblock issues that may arise.

Coach and support our Lead Data Scientists, providing direct line management and supporting the Leads to manage their Data Scientists. Our team are based at both Leeds and India, necessitating ability to perform this role remotely where required.

What you'll have:

You'll be highly numerate with a strong analytical background and proven ability to maintain hands-on technical contribution whilst managing a team. You'll also:

Have demonstrable experience in delivering data science initiatives from concept into production, and can detail experience in data pre-processing, feature engineering, and model evaluation.

Have strong experience in communicating complex analytical and technical concepts to business stakeholders.

Be experienced in using Python or similar statistical analysis packages. Have strong SQL skills, with exposure to Snowflake desirable, and the ability to create clear data visualisations in tools such as Tableau. ​

Be able to demonstrate experience in leading data scientists, monitoring the performance of these colleagues and intervening where necessary, and assisting them in their career development.

Have an appreciation of the importance of data governance, and of how to assess and enhance the quality of our data.

Given the pace of change in new technologies and techniques, show commitment to keeping your knowledge up to date through self-learning, and be supported with opportunities to complete relevant courses and attend industry events.

Have a methodical approach with good attention to detail

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.