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Algorithm Engineer – Financial Services
Oxford Quantum Circuits (OQC). Select, adapt and implement classical algorithms and machine-learning methods for financial-services applications, including fraud detection, credit risk and transaction modelling.
About the role
Key responsibilities & impact- Select, adapt and implement classical algorithms and machine-learning methods for financial-services applications, including fraud detection, credit risk and transaction modelling.
- Collaborate with quantum algorithm engineers and technical product colleagues to combine domain expertise with quantum and hybrid methods.
- Integrate algorithmic components into useful applications.
- Build production-relevant classical detection and prediction baselines for comparison with quantum and hybrid approaches.
- Define classical synthetic-data baselines for assessing quantum-generated data.
- Evaluate practical usefulness, privacy, calibration and robustness over time, considering operational requirements and customer acceptance.
- Communicate findings, limitations and performance trade-offs clearly.
- Help the wider team make evidence-based decisions about approach suitability.
- Work within the Product team and report to Product and Engineering leadership.
- Collaborate across algorithm engineering, software and technical product development.
Requirements
What you’ll need- Experience in quantitative research, applied science or algorithm engineering within banking, fintech or payments.
- Strong expertise in one or more of fraud detection, anti-money laundering (AML), credit risk, market risk, transaction modelling, rare events or financial time series.
- Understanding of operational metrics, model-risk requirements and the consequences of false positives and false negatives.
- Hands-on experience implementing and evaluating algorithms or machine-learning methods, with a clear understanding of practical financial-services needs.
- Ability to take ownership, communicate clearly and collaborate effectively across technical and non-technical disciplines.
- A degree or equivalent practical experience in quantitative finance, mathematics, computer science or a related discipline.
- Experience helping turn algorithms into commercially used applications or products is desirable.
- Relevant research experience connected to business users and their needs is desirable.
- Experience working on enterprise platforms, data products or regulated customer deployments is desirable.
- Familiarity with quantum computing or hybrid classical–quantum products is desirable.
- A relevant professional or postgraduate qualification is desirable.
- Pragmatic approach to problem-solving, balancing technical rigour with practical delivery.
- Curiosity about emerging technologies and their potential applications in financial services.
- Comfort working in a fast-evolving technical environment.