Head of Data Science

Xcede
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Head of Data Science

Head of Data Science

Head of Data Science - Fintech

Head of Data Science

Head of Data Science

Head of AI

x3–4 days a week in the office (nearest station: Camden Town)


About the Role & Company

This high-growth technology company has rapidly scaled a platform that blends on-demand solutions with intelligent optimisation. It helps thousands of businesses improve work management, service quality, and operational performance in real time.


After launching a new AI product that has already reached seven-figure annual recurring revenue within its first year, the business is now preparing for further UK expansion and a move into international markets. They are hiring a Head of AI / Data Science to take things to the next level. This person will lead technical direction, guide a growing team, and remain closely involved in delivering machine learning systems that power real-world impact across the platform.


You’ll shape the roadmap, lead from the front, and play a key role in embedding intelligence into every aspect of product and strategy.


What You’ll Be Doing

  • Set the overall AI strategy and ensure it connects clearly to product outcomes, user value, and business growth
  • Lead and support a team of Data Scientists and ML Engineers while maintaining a strong personal technical contribution
  • Design and scale ML solutions focused on forecasting, optimisation, and real-time performance enhancement
  • Build the infrastructure needed to support experimentation, training, and deployment of production-grade models
  • Evaluate new modelling approaches including reinforcement learning, multimodal architectures, and agentic methods
  • Work closely with Product, Engineering, and Commercial teams to ensure AI delivers measurable results
  • Act as the internal champion for intelligence, influencing how the company makes use of data and automation at every level


What They’re Looking For

  • Circa 6-12 years of experience in ML, AI, or applied data science, with a track record of technical leadership
  • A strong academic foundation in a quantitative or technical subject, ideally including postgraduate study
  • Hands-on experience bringing ML systems from research through to live deployment
  • Strong Python programming skills and experience with libraries such as PyTorch, TensorFlow, or Hugging Face
  • Good understanding of infrastructure and deployment, ideally in cloud environments such as AWS
  • Exposure to areas such as time-series modelling, optimisation, computer vision, or reinforcement learning
  • Confident working across technical and non-technical teams and communicating complex ideas clearly
  • Motivated by impact, scalability, and helping an organisation make intelligence a core capability


If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via (feel free to include a CV for review).

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.