Senior Data Scientist, Risk & ML Ops (Credit)

Zilch; City of Westminster; On-site; GBP 80,000 - 110,000; Full time

Job summary

Zilch in the United Kingdom is seeking a senior Data Scientist to join the Risk team, focusing on developing and deploying advanced credit risk models, with emphasis on collections optimisation and fraud detection. You will work with cross‑functional partners to translate data into production-ready decisioning and risk strategies.

This is a hands-on role for someone who combines strong applied machine learning with modern ML Ops practices, taking models from exploration through to production,

Qualifications

• 3+ years of hands-on data science experience in risk, fraud, or payments.
• Proficiency in SQL, Python and core ML libraries (NumPy, Pandas, Scikit-Learn).
• Experience with production ML, cloud-based model training, deployment and monitoring.
• Strong ability to communicate complex ideas to non-technical audiences.
• Solid engineering mindset; write clean, production-ready code with Git and CI/CD.

Responsibilities

• Work with large, complex datasets to solve problems using statistical approaches.
• Develop strategies for products serving millions of customers and thousands of merchants.
• Build, validate, deploy, and monitor ML models and pipelines across the risk lifecycle.
• Present data science findings to senior stakeholders clearly.
• Apply ML methods to risk and product problems to improve decision-making.
• Contribute to coding practices, testing, and model deployment workflows.
• Conduct A/B testing and experimental analyses to evaluate features and risk strategies.
• Collaborate with engineering to operationalise models and improve production reliability.

Skills

SQL

Python

NumPy

Pandas

Scikit-Learn

ML Ops

Model deployment

A/B testing

CI/CD

Git

Tools

DBT

Looker

SageMaker

Job description

Zilch in the United Kingdom is seeking a senior Data Scientist to join the Risk team, focusing on developing and deploying advanced credit risk models, with emphasis on collections optimisation and fraud detection. You will work with cross‑functional partners to translate data into production-ready decisioning and risk strategies.

This is a hands-on role for someone who combines strong applied machine learning with modern ML Ops practices, taking models from exploration through to production,