Data Science Manager

iO Associates
Leicester
11 months ago
Applications closed

Related Jobs

View all jobs

Data Science / Data Mining Specialist

Data Scientist - 60k - 80k - Leeds (Hybrid) - AI / FinTech SaaS

Data Scientist

Data Analyst | Hybrid | Immediate start

Data Scientist / Statistician (Model Developer)

Data Scientist / Statistician (Model Developer)

Data Science Manager / Up to £100,000 / Permanent / 2 days a week onsite

We are looking for aData Science Managerto join a growingData Science teamwithin a leading eCommerce organisation. This is an exciting opportunity to drive significant commercial value in a fast-paced environment.

This role will focus on optimising how we present content to customers-ensuring the right products are surfaced at the right time and through the right channels. We are looking for a highly skilled data scientist with a strong technical foundation and excellent communication skills, combined with a passion for applying data science to real-world commercial challenges.

This is a hybrid role, offering a mix of office and remote working. The company's main headquarters are based inLeicestershire, and we welcome applicants from across the UK.

About the Role

Collaborate with teams across the business to understand challenges and own the technical solutions, identifying further opportunities to deliver value. Search optimisation - vector embedding of search terms and product items Deep learning and regression modelling for product profitability forecasts Work closely with data engineering and software development teams to define technical requirements and ensure timely delivery. Analyse large volumes of data from various sources, including transactional, demographic, and online data, to build predictive models. Apply machine learning techniques to personalise customer experiences and optimise content presentation. Design and execute robust testing strategies to validate hypotheses and measure commercial impact. Present insights and recommendations to senior stakeholders, including C-suite executives. Proactively identify opportunities for personalisation and customer experience improvements.

About You

Strong expertise in a broad range ofdata science techniques, including regression, classification, and machine learning. Experience with deep learning or generative AI is a plus but not essential. Proficiency in(Spark)SQL and Python. Experience with PySpark is beneficial but not required. Experience designing and implementing robusttesting frameworks. Strong analytical skills with keen attention to detail. Excellent communication skills-comfortable presenting insights to a variety of audiences and crafting a compelling data-driven narrative. Effective time management and ability toprioritise multiple projects. Enthusiastic and eager to learn, with a collaborative yet self-sufficient working style.

This is an exciting opportunity to play a pivotal role in shapingdata-driven customer experiencesfor aleading eCommerce business. If you're passionate about data science and looking for a role where you can make a real commercial impact, we'd love to hear from you!

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.