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- Tasks: Lead a team to develop and deploy impactful ML models in fintech.
- Company: Join a leading fintech company in the UK with a focus on innovation.
- Benefits: Enjoy a competitive package and flexible work environment.
- Why this job: Make a real impact in the fintech industry with cutting-edge ML technology.
- Qualifications: Extensive ML experience, strong team management, and Python coding skills.
- Other info: Collaborative culture with opportunities for continuous improvement and growth.
The predicted salary is between 48000 - 72000 £ per year.
A leading fintech company in the United Kingdom is hiring a Machine Learning Manager to oversee the development and deployment of impactful ML models. You will lead a collaborative team of ML engineers, manage hiring and team development, and ensure continuous improvement of models based on customer data.
The ideal candidate will have:
- Extensive ML experience
- Strong team management skills
- The capability to write production-quality Python code
This role includes a competitive package and offers a flexible work environment.
ML Engineering Manager — Risk Decisioning & LLMs in London employer: cleo
Contact Detail:
cleo Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineering Manager — Risk Decisioning & LLMs in London
✨Tip Number 1
Network like a pro! Reach out to folks in the fintech space, especially those working with ML. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects and any impactful models you've developed. This is your chance to shine and demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and team management skills. Be ready to discuss how you’ve led teams and improved models based on data – real examples will make you stand out!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace ML Engineering Manager — Risk Decisioning & LLMs in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in machine learning and team management. We want to see how your skills align with the role, so don’t be shy about showcasing your Python prowess and any impactful projects you've led.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you’re passionate about ML and how you can contribute to our team. Be specific about your past experiences and how they relate to the job description.
Showcase Your Leadership Skills: As a Machine Learning Manager, we’re keen on seeing your leadership style. Share examples of how you’ve successfully managed teams and fostered collaboration in previous roles. We love a good story!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Plus, it’s super easy!
How to prepare for a job interview at cleo
✨Know Your ML Models Inside Out
Make sure you’re well-versed in the latest machine learning models and techniques, especially those relevant to risk decisioning and LLMs. Be prepared to discuss your past projects, the challenges you faced, and how you overcame them.
✨Showcase Your Team Management Skills
Since this role involves leading a team, be ready to share examples of how you've successfully managed teams in the past. Highlight your approach to hiring, mentoring, and fostering collaboration among team members.
✨Demonstrate Your Coding Proficiency
Brush up on your Python skills, as you'll need to write production-quality code. Consider preparing a small coding exercise or discussing a project where you implemented a complex algorithm to showcase your technical abilities.
✨Emphasise Continuous Improvement
Discuss how you’ve used customer data to refine and improve ML models in previous roles. Be specific about the metrics you tracked and the impact your improvements had on the business.
