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- Tasks: Explore AI and ML model behaviour in banking, translating research into real-world applications.
- Company: Join Starling Bank, the UK's first digital bank, revolutionising banking with technology and fair service.
- Benefits: Enjoy hybrid working, private health insurance, pension schemes, and wellness initiatives.
- Why this job: Be part of a fast-paced, innovative culture that values teamwork and empowers your ideas.
- Qualifications: Strong background in machine learning, programming skills in Python, and experience in production environments required.
- Other info: Diversity is valued; apply even if you don’t meet every requirement.
The predicted salary is between 36000 - 60000 £ per year.
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Machine Learning Applied Scientist (Machine Learning Observability & Governance), London
Client: Starling Bank
Location: London, United Kingdom
Job Category: Other
EU work permit required: Yes
Job Reference: f259df9574f0
Job Views: 17
Posted: 12.08.2025
Expiry Date: 26.09.2025
Job Description:
Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.
We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way.
We’re a fully licensed UK bank with a culture of a fast-moving, disruptive tech company. We’re fairer, easier to use, and designed to demystify money for everyone. We employ more than 3,000 people across London, Southampton, Cardiff, and Manchester.
Our technologists are at the heart of Starling, working in a fast-paced environment focused on building innovative fintech solutions. We operate a flat structure to empower decision-making, fostering a culture of innovation and collaboration. Support is available across teams, emphasizing teamwork and shared goals.
Success at Starling requires being self-driven, taking ownership of your work—from building and designing to sharing knowledge and optimizing processes—to deliver the best outcomes for our customers. Our core values are Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.
Hybrid Working
We have a hybrid approach: we prefer employees to be within a commutable distance to our offices, with a minimum of 1 day per week in the office for Technology teams.
Our Data teams focus on delivering impactful insights across Banking Services, Customer Identity & Financial Crime, and Data & ML Engineering. We value engagement, care for customers, and a broad skill set to tackle diverse challenges.
Responsibilities:
- Pioneering novel methods to understand AI and ML model behaviour in production banking environments.
- Translating research into scalable, maintainable production systems.
- Presenting research findings internally and externally to foster continuous learning.
- Collaborating with data scientists, ML engineers, and stakeholders to address challenges in ML observability and governance.
Requirements:
- Strong academic background and practical experience in advanced machine learning and AI.
- Research experience in academia or industry with leadership in research projects.
- Proven experience deploying ML/AI models in production environments.
- Strong programming skills in Python and familiarity with relevant libraries.
Beneficial but not essential:
- Experience presenting at conferences.
- Understanding of financial services sector and AI applications therein.
- Experience with monitoring and governance of Large Language Models (LLMs).
Our interview process is conversational, designed to be mutually informative, including stages like initial chat, a take-home challenge, technical interviews, and final discussions with executives. We offer a comprehensive benefits package including holidays, private health insurance, pension schemes, and various wellness initiatives.
About us
We encourage applicants to apply even if they don’t meet every requirement. We value diversity and are committed to reshaping banking through our talented team. Join us in solving problems and making banking better for everyone.
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Machine Learning Applied Scientist (Machine Learning Observability & Governance) employer: Starling Bank
Contact Detail:
Starling Bank Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Applied Scientist (Machine Learning Observability & Governance)
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning observability and governance. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the fintech space, especially those working at Starling Bank or similar companies. Attend industry meetups or webinars to build connections that could provide insights or referrals.
✨Tip Number 3
Prepare to discuss your previous projects in detail, particularly those involving deploying ML models in production. Be ready to explain your thought process, challenges faced, and how you overcame them.
✨Tip Number 4
Showcase your passion for continuous learning by mentioning any recent courses or certifications related to AI and machine learning. This demonstrates your commitment to staying updated in a fast-evolving field.
We think you need these skills to ace Machine Learning Applied Scientist (Machine Learning Observability & Governance)
Some tips for your application 🫡
Understand the Role: Read the job description thoroughly to grasp the responsibilities and requirements of the Machine Learning Applied Scientist position. Tailor your application to highlight relevant experiences and skills that align with Starling Bank's mission and values.
Highlight Relevant Experience: Emphasise your academic background and practical experience in advanced machine learning and AI. Include specific examples of projects where you deployed ML/AI models in production environments, showcasing your programming skills in Python and familiarity with relevant libraries.
Showcase Your Research Skills: If you have research experience, detail your contributions and any leadership roles in research projects. Mention any presentations at conferences or publications, as this can set you apart from other candidates.
Craft a Compelling Cover Letter: Write a cover letter that reflects your passion for the role and the company. Discuss how your values align with Starling Bank's core values such as 'Do The Right Thing' and 'Aim For Greatness', and express your enthusiasm for contributing to their innovative fintech solutions.
How to prepare for a job interview at Starling Bank
✨Understand the Company Culture
Before your interview, take some time to research Starling Bank's culture and values. Familiarise yourself with their core principles like 'Listen', 'Keep It Simple', and 'Aim For Greatness'. This will help you align your answers with what they value in their employees.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning and AI in detail. Highlight specific projects where you've deployed models in production environments, and be ready to explain the challenges you faced and how you overcame them.
✨Prepare for Collaborative Scenarios
Since collaboration is key at Starling, think of examples from your past experiences where you worked effectively with data scientists or engineers. Be ready to discuss how you contributed to team goals and shared knowledge within your team.
✨Practice Your Presentation Skills
Given that presenting research findings is part of the role, practice explaining complex concepts in a simple way. You might be asked to present your previous work or ideas during the interview, so clarity and confidence are essential.
