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# Senior Data Scientist

- **Company:** Zilch
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £92,063.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Neural Networks, Continuous Delivery, Continuous Integration, Data Mining, Fraud Prevention and Detection, Github, Python (Programming Language), Machine Learning, NumPy, Rule Engine, Software Safety, SQL Databases, Software Organization, Deep Learning, Git, Pandas, Scikit Learn, Production Code, Machine Learning Operations, Looker Analytics, Software Version Control, Software Library
- **Published:** October 7, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5916638512

## About the Role

* 3+ years of hands-on experience as a data scientist, with a focus on building and deploying models to enhance the customer product experience, risk decisioning, and business outcomes, ideally within credit risk, collections, fraud, financial services, lending, payments, or another decision-intensive domain.
 * Proficiency in SQL, Python and core data science and machine learning libraries, such as NumPy, Pandas and Scikit-Learn.
 * Strong practical experience with machine learning model development, deployment, monitoring, and iteration in production environments, including cloud-based model training, archiving, serving, endpoint deployment, and tools such as Amazon SageMaker or similar.
 * Experience communicating complex ideas to non-technical audiences and senior stakeholders.
 * Strong understanding of machine learning methods, their application in real-world scenarios and a keen awareness of their limitations.
 * A strong engineering mindset, with the ability to write clean, maintainable, production-ready code, use version control systems such as Git, and collaborate effectively in a multi-developer environment.
 * A results-driven approach, with the ability to quickly iterate and improve models in production.
 * Knowledge of AI safety considerations, such as bias detection, privacy management, and handling personally identifiable information (PII).
 * Familiarity with software development best practices and continuous integration/continuous delivery (CI/CD) pipelines, such as GitHub Actions.
 * Experience using DBT for data modelling and Looker for BI reporting.
 * An interest in staying up to date with evolving technologies and applying them to enhance business outcomes.

The following are bonuses rather than strict requirements. We do not expect candidates to have all of them:

 * Prior experience in credit risk modelling, collections optimisation, fraud detection, or risk strategy.
 * Familiarity with risk model governance, explainability, fairness, affordability, or regulatory considerations in financial services.
 * Experience with decision science, automated decisioning systems, decision engines, or risk strategy implementation.
 * Experience applying neural networks or deep learning approaches to practical risk, fraud, or decisioning problems.

## Description

* Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve millions of customers and thousands of merchants.
 * Build, validate, deploy, and monitor robust, scalable machine learning models and model pipelines across the risk lifecycle, including onboarding, affordability, life-time value, credit/default risk, in-life risk, collections, and fraud.
 * Present complex data science findings and methodologies to senior stakeholders clearly and concisely.
 * Apply machine learning methods to solve risk and product-related business problems and enhance our decision-making processes.
 * Contribute to the team's coding efforts, ensuring best practices in version control, testing, CI/CD, model deployment, monitoring, methodologies, workflows, and tooling.
 * Conduct A/B testing, champion/challenger testing, and experimental analyses to evaluate new features, risk strategies, model changes, and product changes.
 * Partner closely with engineering and platform teams to operationalise models, automate workflows, and improve model reliability in production.