Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Staff Data Scientist

Ravelin
London
5 months ago
Applications closed

Related Jobs

View all jobs

Staff Data Scientist

Staff Data Scientist

Staff Data Scientist

Staff Data Scientist — CLV & Next Best Action (Hybrid)

Staff Data Scientist, EMEA

Junior Data Scientist

Who are we?

Hi! We are Ravelin! We're a fraud detection company using advanced machine learning and network analysis technology to solve big problems. Our goal is to make online transactions safer and help our clients feel confident serving their customers.

And we have fun in the meantime! We are a friendly bunch and pride ourselves in having a strong culture and adhering to our values of empathy, ambition, unity, and integrity. We really value work/life balance and we embrace a flat hierarchy structure company-wide. Join us and you’ll learn fast about cutting-edge tech and work with some of the brightest and nicest people around - check out our Glassdoor reviews.

If this sounds like your cup of tea, we would love to hear from you! For more information check out our blog to see if you would like to help us prevent crime and protect the world's biggest online businesses.

The Team

You will be joining the Detection team. The Detection team is responsible for keeping fraud rates low – and clients happy – by continuously training and deploying machine learning models. We aim to make model deployments as easy and error-free as code deployments. Google’s Best Practices for ML Engineering is our bible.

Our models are trained to spot multiple types of fraud, using a variety of data sources and techniques in real time. The prediction pipelines are under strict SLAs; every prediction must be returned in under 300ms. When models are not performing as expected, it’s down to the Detection team to investigate why.

The Detection team is core to Ravelin’s success. They work closely with the Data Engineering Team who build infrastructure and the Intelligence & Investigations Team who liaise with clients.

The Role

We are looking for a Staff Data Scientist to act as a technical leader on behalf of the Detection team, working on solving our most complex challenges. You will use your deep expertise to guide the team in investigating ambiguous cases and ensuring the fundamental integrity of our modeling approaches.

We have to build robust models that are capable of updating their beliefs when they encounter new methods of fraud. You will have the autonomy and influence to shape our technical roadmap and be provided with the resources you need to build world-class fraud detection models. You will not only tackle our most challenging greenfield research but also define the principles and practices that make our incremental improvements safe, scalable, and impactful.

Responsibilities

  • Lead the architectural design and evolution of our model evaluation and training infrastructure, ensuring scalability and best practices.
  • Spearhead the research, development, and deployment of novel modeling approaches for our most complex fraud problems, setting new standards for performance and robustness.
  • Mentor and develop other data scientists on the team, fostering a culture of technical excellence and innovation.
  • Influence technical and product strategy by collaborating with leadership across Data Science, Engineering, and Product.
  • Act as the final escalation point for the team's most challenging model performance and production issues, mentoring others in advanced debugging strategies.
  • Proactively identify and research emerging fraud typologies and cutting-edge machine learning techniques, setting the strategic direction for our fraud detection capabilities.
  • Drive significant improvements in our production ML infrastructure by identifying and implementing key features and architectural changes.

Requirements

  • Extensive, hands-on experience and a proven track record of designing, building, and deploying impactful ML platforms & ML models in a production environment.
  • Experience influencing the technical roadmap of adjacent teams, such as Data Engineering and Product, to build a more effective and cohesive ML ecosystem.
  • Deep expertise in data science and engineering best practices (version control, CI/CD, testing, observability) and a history of applying them to build robust, scalable machine learning systems.
  • Exceptional analytical and problem-solving skills, with a demonstrated ability to define and solve highly ambiguous, complex problems.
  • Proven ability to lead and influence cross-functional teams, driving complex projects to completion.
  • Exceptional communication skills, with the ability to articulate complex technical concepts and strategic rationale to a wide range of audiences, from junior engineers to executive leadership.
  • A high degree of autonomy and the ability to proactively identify, prioritize, and drive opportunities with significant business impact.
  • Being comfortable working with a hybrid team.

Nice to Haves

  • Experience with Go, C++, Java, or another systems language.
  • Experience with Docker, Kubernetes, and ML production infrastructure.
  • PyTorch/Tensorflow deep learning experience.
  • Experience using dbt

Benefits

  • Flexible Working Hours & Remote-First Environment — Work when and where you’re most productive, with flexibility and support.
  • Comprehensive BUPA Health Insurance — Stay covered with top-tier medical care for your peace of mind.
  • £1,000 Annual Wellness and Learning Budget — Prioritise your health, well-being and learning needs with funds for fitness, mental health, and more.
  • Monthly Wellbeing and Learning Day — Take every last Friday of the month off to recharge or learn something new, up to you.
  • 25 Days Holiday + Bank Holidays + 1 Extra Cultural Day — Enjoy generous time off to rest, travel, or celebrate what matters to you.
  • Mental Health Support via Spill — Access professional mental health services when you need them.
  • Aviva Pension Scheme — Plan for the future with our pension program.
  • Ravelin Gives Back — Join monthly charitable donations and volunteer opportunities to make a positive impact.
  • Fortnightly Randomised Team Lunches — Connect with teammates from across the company over in person or remote lunches every other week, on us!
  • Cycle-to-Work Scheme — Save on commuting costs while staying active.
  • BorrowMyDoggy Access — Love dogs? Spend time with a furry friend through this unique perk.
  • Weekly Board Game Nights & Social Budget — Unwind with weekly board games or plan your own socials, supported by a company budget.

*Job offers may be withdrawn if candidates do not meet our pre-employment checks: unspent criminal convictions, employment verification, and right to work*


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.