Senior Data Scientist

Zilch

. Develop and optimise advanced risk models, primarily across credit risk, with additional focus on collections optimisation and fraud detection .

Posted 10/6/2026full-timeLondon • United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying advanced risk models, particularly in credit risk and fraud detection, while leveraging machine learning and data analysis to enhance decision-making and operational efficiency. Proficient in communicating complex data insights to stakeholders and collaborating across teams to optimize product strategies.

Highest-signal resume keywords

Machine Learning Model DevelopmentSQL ProficiencyPython ProgrammingCloud-Based Model DeploymentData Analysis and Presentation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical AnalysisData MiningA/B TestingVersion Control (Git)CI/CD PracticesData Modelling (DBT)Business Intelligence (Looker)Quantitative AnalysisProduction-Ready Code

Soft Skills

Communication SkillsCollaborationProblem-Solving

Tools & Technologies

Amazon SageMakerNumPyPandasScikit-LearnGitHub Actions

Industry Keywords

Credit RiskFraud DetectionFinancial ServicesLendingPaymentsOperational EfficiencyCustomer ExperienceRisk DecisioningData ScienceAI Safety Considerations

Tech Stack

Tools & technologies

CloudNumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Develop and optimise advanced risk models, primarily across credit risk, with additional focus on collections optimisation and fraud detection
Leverage diverse data sources to build production-grade models and decisioning solutions
Improve risk outcomes, customer experience, and operational efficiency
Work with large and complex datasets using analytical and statistical approaches
Apply quantitative analysis, experimentation, data mining, and data presentation to develop product strategies
Build, validate, deploy, and monitor scalable machine learning models and pipelines across onboarding, affordability, lifetime value, credit/default risk, in-life risk, collections, and fraud
Present complex data science findings and methodologies to senior stakeholders
Apply machine learning to risk and product-related business problems and improve decision-making
Contribute to coding practices covering version control, testing, CI/CD, model deployment, monitoring, workflows, and tooling
Conduct A/B testing, champion/challenger testing, and experimental analyses
Partner with engineering and platform teams to operationalise models, automate workflows, and improve production reliability
Collaborate with product managers, engineers, risk strategy, credit, collections, fraud, and other data scientists

Requirements

What you’ll need

3+ years of hands-on experience as a data scientist
Focus on building and deploying models to enhance customer product experience, risk decisioning, and business outcomes
Experience ideally within credit risk, collections, fraud, financial services, lending, payments, or another decision-intensive domain
Proficiency in SQL, Python, NumPy, Pandas, and Scikit-Learn
Practical experience with machine learning model development, deployment, monitoring, and iteration in production environments
Experience with cloud-based model training, archiving, serving, and endpoint deployment, such as Amazon SageMaker or similar
Experience communicating complex ideas to non-technical audiences and senior stakeholders
Strong understanding of machine learning methods, real-world applications, and limitations
Ability to write clean, maintainable, production-ready code
Experience using version control systems such as Git
Familiarity with software development best practices and CI/CD pipelines, such as GitHub Actions
Experience using DBT for data modelling and Looker for BI reporting
Knowledge of AI safety considerations, including bias detection, privacy management, and handling personally identifiable information (PII)
Interest in staying up to date with evolving technologies and applying them to business outcomes
Must be able to work in the UK; application asks whether visa sponsorship is required to work in the UK
Must live within commutable distance of the London Victoria office and be able to attend the office 3 days per week (Monday, Wednesday, and Thursday, subject to change)

Benefits

Comp & perks

Hybrid work arrangement
Opportunity to work at a fast-growing fintech company
Work on products serving more than 6 million customers and thousands of merchants