Lead Data Scientist

Xcede
Leeds
8 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist

Surrey office, x1 day every two weeks.


A well-established, product-led business is looking for a Lead Data Scientist to spearhead innovation and drive measurable value through advanced machine learning, experimentation, and the development of production-grade models.


Sitting within a cross-functional data team, this is a hands-on leadership role with the autonomy to shape the modelling roadmap, contribute to R&D strategy, and influence pricing and risk decisions across multiple business lines. You’ll manage a small team of data scientists, guiding them through delivery while remaining actively involved in technical implementation and experimentation.


This is a unique opportunity for someone passionate about building machine learning systems that go beyond prototypes — models that deliver real-world commercial outcomes in a data-rich, regulated environment.


Key Responsibilities

  • Lead a high-performing team of data scientists to deliver cross-functional, impactful AI/ML initiatives
  • Design and implement predictive models and machine learning solutions for core business areas
  • Build and productionise models in collaboration with data engineers and platform teams
  • Apply advanced statistical techniques to extract insights and guide product and pricing strategies
  • Work closely with stakeholders to understand requirements, define modelling goals, and demonstrate business value
  • Evaluate vendor data sources, assess economic and technical feasibility, and lead test-and-learn initiatives
  • Contribute to the modelling roadmap, experimentation frameworks, and internal data science tooling
  • Produce clean, maintainable, version-controlled code and refactor solutions into reusable libraries and APIs
  • Coach junior team members and promote best practices across the wider data and analytics community


Requirements


  • Ideally, 6+ years of hands-on experience applying data science techniques in commercial or research-led environments, delivering clear business outcomes
  • Advanced academic background (MSc or PhD) in a technical or quantitative field such as Machine Learning, Computer Science, or Statistics
  • Strong programming ability in Python (data science ecosystem) and SQL, with proven experience handling large, complex datasets
  • Solid track record of building, validating, and deploying machine learning models into real-world systems
  • Practical experience designing experiments, selecting evaluation metrics, and applying multivariate testing frameworks
  • Leadership mindset — you’ve mentored or managed data science colleagues or helped steer technical decisions in a collaborative team
  • Comfortable with version control (Git) and familiar with engineering workflows like CI/CD and containerised environments
  • Skilled at working with both structured and unstructured data to unlock insights and power models
  • Hands-on experience with Databricks, Apache Spark, or similar tools used in large-scale data processing
  • Exposure to machine learning model deployment using APIs or lightweight serving frameworks like Flask or Keras
  • Familiarity with geospatial data would be a great bonus!


If this role interests you and you would like to learn more, 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 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.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 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.