Machine Learning Engineering Manager - Risk Decisioning
Machine Learning Engineering Manager - Risk Decisioning

Machine Learning Engineering Manager - Risk Decisioning

Full-Time 48000 - 84000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Lead a team to build and deploy impactful machine learning models for financial decision-making.
  • Company: Join Cleo, a fast-growing fintech unicorn transforming how people manage money.
  • Benefits: Competitive salary, equity options, flexible work, and generous leave policies.
  • Why this job: Shape the future of finance with cutting-edge technology and a passionate team.
  • Qualifications: Proven experience in ML model deployment and team management.
  • Other info: Dynamic environment with opportunities for personal and professional growth.

The predicted salary is between 48000 - 84000 £ per year.

About Cleo
At Cleo, we’re not just building another fintech app. We’re embarking on a mission to fundamentally change humanity's relationship with money. Imagine a world where everyone, regardless of background or income, has access to a hyper-intelligent financial advisor in their pocket. That’s the future we’re creating.

About The Company
Cleo is a rare success story: a profitable, fast-growing unicorn with over $200 million in ARR and growing over 2x year-over-year. This isn’t just a job; it’s a chance to join a team of brilliant, driven individuals who are passionate about making a real difference. We have an exceptionally high bar for talent, seeking individuals who are not only at the top of their field but also embody our culture of collaboration and positive impact. If you’re driven by complex challenges that push your expertise, the chance to shape something truly transformative, and the potential to share in Cleo’s success as we scale, while growing alongside a company that’s scaling fast, this might be your perfect fit.

About The Role
You’ll join the existing Risk Decisioning data science function here at Cleo; a thoughtful and collaborative team of ML engineers, analysts, and data engineers with significant industry experience that is at the heart of everything we do at Cleo. You’ll build and deploy production models that developers will feed directly into the product. This position is essential in the expansion of both product and business. We are highly data driven, whether that be understanding natural language, deriving insights from financial data, or determining which financial product is best suited to a user. We have interesting problems to solve on an ever-increasing scale. You’ll be working on a hugely impactful workstream, focused on the machine learning that underpins our Chat feature. You’ll be shaping how Cleo uses LLMs to have more engaging, accurate, and personalised conversations about money.

What You’ll Be Doing
- Understanding core problems faced by our Risk Decisioning team and coordinating the ML function’s contribution to solving them.
- Finding opportunities for model and product improvements in Cleo's extensive datasets of customer behaviour.
- You’ll be responsible for people management of the ML engineers in your squads, coaching and developing them to deliver on the roadmap.
- You’ll build out the headcount plan and will be responsible for all hiring and team development within your area to support our growth.

About You
- Extensive experience building and deploying Machine Learning models to production.
- Experience managing and developing high performing teams of data scientists and ML engineers.
- Habits of keeping abreast of the latest research and experimenting productively with new technologies, including LLMs and LLM agent design.
- Experience owning an ML roadmap, driving new ideas through to seeing business impact.
- Ability to write production quality Python code and a willingness to be somewhat hands on.

Nice to haves
- Domain knowledge of NLP, including evaluating LLM systems and fine tuning LLMs or other transformer models.
- A strong ability to communicate findings to non-technical stakeholders in a concise and engaging manner.
- Experience with containers and container orchestration: Kubernetes, Docker, and/or Mesos.
- Experience using vector databases such as OpenSearch.

The recruitment process
- Interview with a Recruiter (30 mins)
- Interview with the Hiring Manager (30 mins)
- Python Programming Interview (45 mins)
- White-boarding session (60 mins)
- Technical Discussion (45 mins)
- Management Skills Interview (60 mins)

What do you get for all your hard work?
- A competitive compensation package (base + equity) with termly reviews, aligned to our quarterly OKR planning cycles.
- Work at one of the fastest-growing tech startups, backed by top VC firms, Balderton & EQT Ventures.
- A clear progression plan. We want you to keep growing. That means trying new things, leading others, challenging the status quo and owning your impact. Always with our complete support.
- Flexibility. We can’t fight for the world’s financial health if we’re not healthy ourselves. We work with everyone to make sure they have the balance they need to do their best work.
- Work where you work best. We’re a globally distributed team. If you live in London we have a hybrid approach, we’d love you to spend one day a week or more in our beautiful office. If you’re outside of London, we’ll encourage you to spend a couple of days with us a few times per year. And we’ll cover your travel costs, naturally.
- Company-wide performance reviews every 4 months.
- Generous pay increases for high-performing team members.
- Equity top-ups for team members getting promoted.
- 6% employer-matched pension in the UK.
- 25 days annual leave a year + public holidays (+ an additional day for every year you spend at Cleo, up to 30 days).
- 1 month paid sabbatical after 4 years at Cleo.
- We’ll pay for your OpenAI subscription.
- Private Medical Insurance via Vitality, dental cover, and life assurance.
- Online mental health support via Spill.
- Enhanced parental leave.
- Workplace Nursery Scheme.
- Regular socials and activities, online and in-person.

We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds.

Machine Learning Engineering Manager - Risk Decisioning employer: cleo

Cleo is an exceptional employer, offering a dynamic work environment where innovation meets purpose. With a strong focus on employee growth, competitive compensation, and a commitment to work-life balance, Cleo empowers its team members to thrive both personally and professionally. The collaborative culture fosters creativity and inclusivity, making it an ideal place for those passionate about transforming the financial landscape while enjoying the benefits of a hybrid work model in London.
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Contact Detail:

cleo Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Machine Learning Engineering Manager - Risk Decisioning

✨Tip Number 1

Network like a pro! Reach out to current or former Cleo employees on LinkedIn. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.

✨Tip Number 2

Prepare for those interviews! Brush up on your Python skills and be ready to discuss your experience with ML models. Practising common interview questions can help you feel more confident and articulate.

✨Tip Number 3

Show your passion for Cleo's mission! During interviews, share how your values align with theirs. Talk about your excitement for using ML to improve financial decision-making for everyone.

✨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, it shows you’re serious about joining the team!

We think you need these skills to ace Machine Learning Engineering Manager - Risk Decisioning

Machine Learning
Model Deployment
Team Management
Data Science
Natural Language Processing (NLP)
Python Programming
LLM Evaluation and Fine Tuning
Container Orchestration (Kubernetes, Docker)
Vector Databases (OpenSearch)
Communication Skills
Problem-Solving
Roadmap Ownership
Research and Experimentation

Some tips for your application 🫡

Show Your Passion: When you're writing your application, let your enthusiasm for machine learning and risk decisioning shine through. We want to see that you’re not just looking for a job, but that you’re genuinely excited about the impact you can make at Cleo.

Tailor Your Experience: Make sure to highlight your relevant experience in building and deploying ML models. We love seeing how your past work aligns with what we do at Cleo, so don’t be shy about showcasing your achievements!

Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, especially when it comes to complex topics like ML. Use simple language to explain your skills and experiences, making it easy for us to understand your fit for the role.

Apply Through Our Website: We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team!

How to prepare for a job interview at cleo

✨Know Your Stuff

Make sure you brush up on your machine learning models and their applications, especially in risk decisioning. Be ready to discuss your past experiences with deploying models and how they impacted the business. This will show that you not only understand the technical side but also the business implications.

✨Showcase Your Leadership Skills

Since this role involves managing a team, be prepared to share examples of how you've successfully led teams in the past. Talk about your coaching style and how you develop high-performing teams. Highlight any specific challenges you've faced and how you overcame them.

✨Communicate Clearly

You’ll need to explain complex concepts to non-technical stakeholders, so practice simplifying your explanations. Use relatable analogies or examples from your experience to make your points clear. This will demonstrate your ability to bridge the gap between technical and non-technical teams.

✨Prepare for Technical Challenges

Expect to tackle some hands-on coding during the interview. Brush up on your Python skills and be ready to solve problems on the spot. Familiarise yourself with container orchestration tools like Kubernetes and Docker, as these might come up in discussions.

Machine Learning Engineering Manager - Risk Decisioning
cleo

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