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For Companies At a Glance
- Tasks: Lead a team to create impactful AI/ML models and drive innovation.
- Company: Join a well-established, product-led business focused on data-driven solutions.
- Benefits: Enjoy flexible working with one day in the office every two weeks.
- Why this job: Shape the future of machine learning in a collaborative, data-rich environment.
- Qualifications: 6+ years in data science, advanced degree, strong Python and SQL skills required.
- Other info: Opportunity to mentor junior team members and influence key business decisions.
The predicted salary is between 43200 - 72000 £ per year.
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 niall.wharton@Xcede.com (feel free to include a CV for review).
Lead Data Scientist employer: Xcede
Contact Detail:
Xcede Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist
✨Tip Number 1
Make sure to showcase your leadership experience in data science. Highlight any instances where you've mentored or managed a team, as this role requires guiding a small team of data scientists.
✨Tip Number 2
Familiarise yourself with the latest advancements in machine learning and statistical techniques. Being able to discuss recent projects or innovations can demonstrate your passion and expertise during interviews.
✨Tip Number 3
Prepare to discuss your experience with productionising models and working with data engineers. This role involves collaboration, so being able to articulate how you've successfully worked in cross-functional teams will be beneficial.
✨Tip Number 4
If you have experience with tools like Databricks or Apache Spark, be ready to share specific examples of how you've used them in large-scale data processing. This knowledge is crucial for the role and can set you apart from other candidates.
We think you need these skills to ace Lead Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, particularly your hands-on work with machine learning models and leadership roles. Use specific examples that demonstrate your ability to deliver business outcomes.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data science and your vision for leading a team. Mention specific projects or achievements that align with the responsibilities of the Lead Data Scientist role.
Showcase Technical Skills: In your application, emphasise your programming skills in Python and SQL, as well as your experience with tools like Databricks and Apache Spark. Provide examples of how you've used these skills to solve complex problems.
Highlight Leadership Experience: Discuss your experience mentoring or managing other data scientists. Share how you’ve contributed to team success and fostered best practices within a collaborative environment.
How to prepare for a job interview at Xcede
✨Showcase Your Leadership Skills
As a Lead Data Scientist, you'll be managing a team. Be prepared to discuss your leadership style and provide examples of how you've successfully guided teams in the past. Highlight any mentoring experiences and how you foster collaboration.
✨Demonstrate Technical Expertise
Make sure to brush up on your technical skills, especially in Python and SQL. Be ready to discuss specific projects where you've built and deployed machine learning models, and be prepared to explain your approach to handling large datasets.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities. Prepare to discuss how you would approach designing experiments or selecting evaluation metrics for a given business challenge. Use real-world examples to illustrate your thought process.
✨Understand the Business Context
Research the company and its products thoroughly. Be ready to discuss how your data science initiatives can drive measurable value and influence pricing and risk decisions. Showing an understanding of the business will set you apart from other candidates.
