Data Science & Machine Learning Engineer

ROLE SUMMARY

As a Data Science & Machine Learning (DSML) Engineer, you will be responsible for maintaining and advancing the 4-Xtra AI-powered extreme values forecasting and synthetic stress scenario generation platforms. The role combines hands-on machine learning research and engineering with production system development, working at the intersection of extreme value theory, synthetic data generation, and modern AI technologies including agentic AI. The critical part of your bookwork will be to continuously translate academic research into production-grade financial risk products in alignment with the company's financial services market focus. You will be working under the line management Academic Co-founders, with close collaboration and guidance from the DSML Head (when appointed) and 4-Xtra Senior Financial Services Advisor (S-FSA).

PRIMARY OBJECTIVES

Research-driven product development - Effectively work on 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 co-ownership - Co-own the production pipeline of the 4-XTRA engine including 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 and DSML Head on mathematical models and implementation, and with the Senior FSA on domain requirements, 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 to assist with the technical representation of the products.

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 - 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.

Product knowledge:

Essential - Advanced 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.

Team: Research & Development

Location: UK or EU based, remote working possible, with occasional in-person onsite meetings as appropriate (in Leeds)

Reports to: Academic Co-founders / Board of Directors

Hours of work: Full-time, flexible working hours by agreement, expected availability for joint activities as and when required, e.g. meetings with the line manager(s) and/or the Board

Term of contract: Up to 12 months; extendable subject to performance

Salary range: Competitive, subject to qualifications and performance

Starting date: 1st December 2026 or as soon as possible thereafter