Senior Risk Data Scientist Remote (London) ML-Driven Risk

Harnham - Data and Analytics Recruitment; Greater London; Remote; GBP 80,000 - 85,000; Full time

Benefits offered by this job

Benefits

Job summary

Harnham - Data and Analytics Recruitment in London is seeking a Senior Risk Data Scientist to develop and sharpen predictive risk capabilities across payments and financial services. You will work at the intersection of data science, ML, and risk management to shape product direction.

You will collaborate with commercial stakeholders and external partners to identify risk signals, deploy models, and monitor performance.

Qualifications

• Experience using Python for data analysis and modelling.
• Advanced SQL skills for large datasets.
• Experience in credit risk, fraud analytics, or payments risk.
• Experience building supervised ML models (logistic regression, gradient boosting).
• Ability to work autonomously in a fast-moving environment.
• Exposure to clustering, anomaly detection, or LLM-related AI is a plus.
• Knowledge of risk-based products in financial services is beneficial.

Responsibilities

• Develop and enhance predictive risk capabilities with commercial stakeholders.
• Build, deploy, and monitor predictive models and risk rules.
• Apply ML and statistical techniques to real-world data challenges.
• Collaborate with external partners to understand requirements.
• Contribute to innovative risk management methodologies and IP.

Skills

Python

SQL

Credit risk

Fraud analytics

Payments risk

Supervised ML models

Logistic regression

Gradient boosting

Autonomy

Problem solving

Fast-paced environment

Clustering

Anomaly detection

LLM applications

Agentic AI

Job description

Harnham - Data and Analytics Recruitment in London is seeking a Senior Risk Data Scientist to develop and sharpen predictive risk capabilities across payments and financial services. You will work at the intersection of data science, ML, and risk management to shape product direction.

You will collaborate with commercial stakeholders and external partners to identify risk signals, deploy models, and monitor performance.