4-Xtra Technologies Ltd
Head of Machine Learning — Quantitative Risk & Scenario AI
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The Role
Head of Machine Learning
Responsible for leading and advancing 4-Xtra's AI-powered extreme values forecasting and synthetic stress scenario generation platforms, as the senior technical owner of the company's machine-learning ecosystem and its production implementation.
The role combines hands-on machine learning research and engineering with production system ownership, working at the intersection of extreme value theory, synthetic data generation, and modern AI technologies including agentic AI. The successful candidate will continuously translate academic research into production-grade financial risk products. They will be working under the line management of Academic Co-founders (with a combined 50+ years of experience in research and industrial innovation). Familiarity with financial risk concepts is essential to ensure effective collaboration with the company's Senior Financial Services Advisor and alignment with the company's financial services market focus. Interest and capacity to expand the applications of the 4-Xtra ML predictive tools from FinTech to other verticals and application domains (such as HealthTech and environment) is desirable.
Primary Objectives
- Research-driven product development. Lead advanced modelling and AI product development — statistical models, extreme value theory applications, synthetic data generation (SDG), neural network solutions — from research prototype through production deployment. Proactively identify and implement state-of-the-art ML techniques to maintain 4-Xtra's technological competitiveness.
- Platform and codebase ownership. Own the core Python backend codebase supporting the AWS environment (EC2, S3, Lambda, RDS, Elastic Beanstalk), ensuring reliability, scalability, security, and maintainability. Manage CI/CD pipelines and GitLab administration.
- AI-native development. Leverage agentic AI tools, LLM-assisted coding, and modern AI development workflows across the full development lifecycle — code generation, testing, documentation, and infrastructure automation. Continuously evaluate and integrate emerging AI capabilities to accelerate delivery velocity.
- Cross-functional collaboration. Work closely with Academic Co-founders on mathematical models and implementation, and with the Senior Financial Services Advisor on domain requirements — a named, first-class responsibility of this role, including support to customer-traction activity. Translate quantitative models into product features aligned with financial industry use cases. Participate in client demonstrations and stakeholder engagements as the technical voice of the product.
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Personal Specification
Qualifications & Training:
- Essential: MSc or PhD in Statistics, Machine Learning, Computer Science, Mathematics, Physics, or related quantitative discipline.
- Desirable: PhD with published research in machine learning, extreme value theory, synthetic data, or statistical modelling.
Experience:
- Essential: Strong track record building and owning production ML systems and cloud-hosted platforms. Deep Python backend experience. Proven ability to work across research, engineering, and infrastructure in a lean team. Demonstrated ability to translate academic research into working software. Ability to inherit and extend an existing production research codebase at pace.
- Desirable: Experience with financial services data, risk models, or regulatory scenarios. Prior work in a startup or early-stage company. Familiarity with financial risk concepts (stress testing, VaR, scenario analysis) sufficient to collaborate with domain experts.
Qualities & Attitude:
- Essential: Research-hungry and intellectually curious — proactively seeks state-of-the-art techniques. Practical, accountable, and comfortable operating across theory, coding, infrastructure, and delivery. Self-directed with strong judgement.
- Desirable: Comfortable engaging with financial industry stakeholders (CROs, risk managers, regulators). Effective at explaining complex technical concepts to non-technical audiences.
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Product Knowledge:
- Essential: Expert knowledge of Python, AWS, Git/GitLab, Linux, CI/CD, SQL, and modern ML/statistical methods. Strong grasp of generative models (diffusion, GANs, VAEs), transformers, and probabilistic modelling. Proficiency with AI-assisted development tools (LLM coding assistants, agentic workflows) across the full stack.
- Desirable: Experience with financial data feeds (Bloomberg, Refinitiv) or risk platforms. Frontend development capability (JavaScript/React). Familiarity with extreme value theory and/or tail risk modelling.
Key Competencies
Advanced Modelling & Research:
- Essential: Strong grounding in statistical modelling, machine learning, and applied research with ambition to push technological boundaries.
- Desirable: AI system development: Ability to design, evaluate, and productionise neural network and generative AI solutions including agentic architectures.
Engineering & Platform Ownership:
- Essential: Ability to build, maintain, and improve Python services, repositories, and cloud infrastructure end-to-end.
- Desirable: Delivery management: Able to scope work, manage priorities, and deliver through direct ownership in a resource-constrained environment.
Stakeholder Communication:
- Essential: Able to work with technical and non-technical stakeholders, clarify requirements, and communicate trade-offs.
- Desirable: PoC execution: Able to turn research ideas into working technical demonstrations for financial industry audiences.
What We Offer
- Location: UK or EU based, remote working possible, with occasional in-person onsite meetings as appropriate at the University of Leeds.
- Hours of work: Full-time, flexible working hours by agreement.
- Salary: Competitive, subject to qualifications, experience, and performance.
- Employment Benefits: Attractive package.
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