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- Tasks: Lead a team to develop impactful ML and AI solutions.
- Company: Join a fast-scaling company transforming lives with innovative software.
- Benefits: Enjoy a competitive salary, hybrid work, and flexible hours.
- Why this job: Make a real-world impact while tackling exciting technical challenges.
- Qualifications: Strong Python skills and experience in leading data science projects required.
- Other info: Opportunity to shape applied AI in a rapidly growing company.
The predicted salary is between 68000 - 102000 £ per year.
About the Company
I’m working with a fast-scaling company that recently secured £20M in Series A funding and is already preparing for Series B. They’re building software that genuinely changes lives. With expansion underway, especially into the US, it’s a brilliant time to join. The team is sharp, diverse, and growing quickly, with plenty of momentum behind what they’re building.
About the Role
This is a permanent, full-time Data Science Team Leader position. You’ll head a team developing production-grade ML and AI solutions, focusing on performance, rigour, and real-world impact. The role is hands-on and strategically important. You’ll own end-to-end delivery, collaborate with technical and scientific teams, and help define how data science is applied across the business. This is meaningful AI work, with clear technical challenges and purpose.
Responsibilities
- Lead and mentor a team of data scientists working on applied ML projects
- Take ownership of projects from data acquisition to deployment
- Develop robust, high-performance models for real-world datasets
- Ensure all outputs meet technical and regulatory standards
- Work closely with scientists, engineers, and domain experts
- Introduce new tools, methods, and approaches where relevant
- Communicate clearly with both technical and non-technical audiences
Qualifications
- Strong Python skills and practical machine learning experience
- Proven ability to lead or mentor in a data science setting
- Track record of delivering models into production environments
- Degree in a relevant technical field or equivalent hands-on experience
- Excellent written and verbal communication skills
- Bonus if you’ve worked with MLflow, GitHub, PyTest, Jira, or HPC systems
- Bonus if you have experience under a QMS or with safety-critical software
What’s in it for You?
- £85,000 depending on experience
- Hybrid working model with two days a week in the office
- Flexible hours around core team collaboration
- A leadership role with real-world impact and technical challenge
- The chance to shape applied AI in a company that’s scaling fast
If you’re interested in leading a high-performing team, solving hard problems, and applying data science where it truly matters, I’d love to hear from you. Send your CV and a short note to the Chief Scientific Officer at the email provided.
Data Science Team Leader employer: erg group
Contact Detail:
erg group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Team Leader
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and AI, especially those that align with the company's focus on real-world impact. This will not only help you understand their needs better but also allow you to discuss relevant topics during interviews.
✨Tip Number 2
Showcase your leadership experience by preparing examples of how you've successfully mentored or led teams in previous roles. Be ready to discuss specific challenges you faced and how you overcame them, as this will demonstrate your capability to lead a data science team.
✨Tip Number 3
Network with professionals in the data science field, particularly those who have experience in production environments. Engaging with industry peers can provide insights into the role and may even lead to referrals, increasing your chances of landing the job.
✨Tip Number 4
Prepare to discuss your experience with tools like MLflow, GitHub, and Jira, as these are mentioned as bonuses in the job description. Being able to articulate how you've used these tools in past projects will set you apart from other candidates.
We think you need these skills to ace Data Science Team Leader
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in leading data science teams and your practical machine learning skills. Emphasise any projects where you've taken ownership from data acquisition to deployment.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data science and leadership. Mention specific examples of how you've mentored others and delivered models into production, aligning your experiences with the company's mission.
Highlight Relevant Skills: In your application, clearly outline your strong Python skills and any experience with tools like MLflow, GitHub, or Jira. This will demonstrate your technical proficiency and readiness for the role.
Showcase Communication Skills: Since the role requires clear communication with both technical and non-technical audiences, include examples in your application that illustrate your ability to convey complex information effectively.
How to prepare for a job interview at erg group
✨Showcase Your Leadership Skills
As a Data Science Team Leader, it's crucial to demonstrate your ability to lead and mentor. Prepare examples of how you've successfully guided teams in the past, focusing on your approach to fostering collaboration and innovation.
✨Highlight Technical Expertise
Make sure to discuss your strong Python skills and practical machine learning experience. Be ready to talk about specific projects where you developed robust models and how you ensured they met technical standards.
✨Communicate Clearly
Since you'll be working with both technical and non-technical audiences, practice explaining complex concepts in simple terms. This will show your ability to bridge the gap between different teams and stakeholders.
✨Prepare for Real-World Scenarios
Expect questions that assess your problem-solving skills in real-world contexts. Think of challenges you've faced in previous roles and how you overcame them, particularly in delivering models into production environments.
