Senior Risk Data Scientist

Senior Risk Data Scientist

Remote (London) | £80,000 - £85,000 + Benefits

This is an opportunity to join an innovative data-driven business that is transforming how risk is assessed and managed within the payments ecosystem. Working at the intersection of Data Science, Machine Learning, and FinTech, you will have the chance to solve complex commercial challenges, influence product development, and help shape cutting-edge risk solutions from the ground up.

The Company

They are a fast-growing technology business focused on helping organisations better understand and manage risk through advanced analytics and machine learning. Their proprietary solutions combine large-scale transactional data with external data sources to deliver actionable insights and predictive risk intelligence. Operating across international markets, they offer a collaborative environment where data science plays a central role in business success.

The Role

As a Senior Data Scientist, you will be responsible for developing and enhancing predictive risk capabilities while working closely with commercial stakeholders and external partners.

Key responsibilities include:

• Using advanced analytics and investigative techniques to identify, monitor, and understand emerging risk trends across customer portfolios.
• Building, deploying, and monitoring predictive models and risk rules to improve decision-making and risk assessment.
• Applying machine learning and statistical techniques to complex real-world data challenges.
• Collaborating with external stakeholders to understand requirements and influence product development.
• Contributing to the development of innovative risk management methodologies and intellectual property.

Your Skills & Experience

• Strong commercial experience using Python for data analysis and modelling.
• Advanced SQL skills for extracting, transforming, and analysing large datasets.
• Experience working within credit risk, fraud analytics, payments risk, or a related quantitative environment.
• Practical experience building supervised machine learning models such as logistic regression and gradient boosting models.
• Ability to work autonomously, solve problems proactively, and thrive in a fast-moving environment.
• Exposure to clustering, anomaly detection, LLM applications, or agentic AI approaches would be advantageous.
• Strong commercial understanding of risk-based products, financial services, or payments environments is beneficial.

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