Role Description As a Staff ML Scientist, you’ll be the most senior Individual Contributor (IC) Machine Learning Scientist across the entire FinCrime collective! This will give you a real opportunity to lead us into an exciting new phase of fraud and financial crime prevention, utilising billions of rows of data and the learnings from your previous successes in designing and building advanced Machine Learning based real time detection systems. More specifically, we’ll be expecting you to leverage your deep experience of developing and deploying advanced Machine Learning models within the fields of financial crime, fraud, security, or trust and safety to: - Lead our ongoing journey to build an advanced, scalable, extensible, automated fraud and FinCrime detection system that effectively prevents crime while minimising impact to genuine customers and operational costs. - Ensure our detection systems can adapt quickly and appropriately to changing fraud and financial crime trends, remaining performant through time. Your day-to-day: - Providing key technical leadership and shipping highly impactful ML-based solutions. - Working across the FinCrime collective identifying the most impactful areas and leading solution development. - Collaborating closely with product managers, data scientists, backend engineers and designers in an agile environment. - Advising senior business stakeholders and helping to set and advance our strategic direction in FinCrime. - Steering technical work and driving up standards within the Machine Learning discipline. This will involve: - Identifying and scoping out the most impactful opportunities to tackle Financial Crime and Fraud with Machine Learning. - Leading advancements in our Financial Crime and Fraud detection capabilities, for example utilising deep learning, graph-based, and sequence-based architectures. - Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline. - Working closely with our MLOps team to steer the ongoing development of tools to enable rapid iteration of models and optimisations of the full ML model lifecycle. Qualifications - Multiple year track record of excellence leading the technical work of a team in the development and deployment of advanced Machine Learning models. - Experience developing and shipping deep learning, graph-based, and/or sequence-based ML architectures to production. - Experience in the domain of fraud, financial crime, security or trust and safety. - Passion for mentoring other ML practitioners and sharing knowledge. - Extensive experience writing production Python code and a strong command of SQL. - Comfortable using Go lang, which is used in many of our backend microservices. - Adaptable, curious and enjoy learning new technologies and ideas. Requirements - Impact driven and excited to own the end-to-end journey from business problem to solution. - Experience helping your team and stakeholders resolve ambiguity. - Desire to be involved in building a product that you (and the people you know) use every day. - Product mindset: care about customer outcomes and make data-informed decisions. - Excited about fast-moving developments in Machine Learning and can communicate those ideas to colleagues. Benefits - Relocation assistance to the UK. - Visa sponsorship available. - Flexible working hours and trust to work enough hours to do your job well. - Learning budget of £1,000 a year for books, training courses and conferences. - Part-time work options available to help meet other commitments or strike a great work-life balance.

• Define and champion the strategic vision for AI and foundation models • Lead the end-to-end lifecycle of research, design, and deployment for large-scale foundation models • Partner with executive leadership, engineering, product, and data science teams • Mentor senior technical talent and foster research communities • Embed cutting-edge research into products

• Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning products • Take ownership of objectives and key results for your workstream, and own technical solutions in partnership with your manager • Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale • Champion code quality, reusability, scalability, maintainability, and security, and provide input into strategic architecture decisions • Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices • Integrate Machine Learning and AI systems with production applications • Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community

• Develop, test, fine-tune, and optimize computer vision and object detection models using video and image datasets • Support the deployment of ML models in production or near-production environments • Analyze and process large-scale visual datasets, including annotation workflows and video sample preparation • Build new models and adapt pre-trained models to support operational and safety-focused use cases • Evaluate technical feasibility and prioritize AI/ML use cases according to business timelines and delivery goals • Collaborate with cross-functional stakeholders including Infrastructure, Operations, AI Engineering, and business teams to translate operational requirements into technical solutions • Contribute to the development of real-time detection and alerting capabilities using camera-based systems • Support rapid prototyping and MVP delivery initiatives within fast-paced environments