Senior Data Scientist (Fintech)

Location: hirify.global UK; hybrid working

Company

hirify.global is a fast-growing European fintech company developing payment products for millions of customers and thousands of merchants.

What you will do

• Develop, validate, deploy, monitor, and iterate machine learning models across credit risk, affordability, collections, fraud, onboarding, and customer lifecycle use cases.
• Analyse large and complex datasets using quantitative analysis, experimentation, data mining, and statistical methods.
• Build production-grade model pipelines and decisioning solutions that improve risk outcomes, customer experience, and operational efficiency.
• Conduct A/B tests, champion/challenger tests, and experimental analyses for features, risk strategies, models, and product changes.
• Partner with engineering and platform teams to automate workflows and improve model reliability in production.
• Present complex data science findings clearly to senior stakeholders and cross-functional partners.

Requirements

• 3+ years of hands-on data science experience building and deploying models in production.
• Strong proficiency in Python, SQL, NumPy, Pandas, and Scikit-Learn.
• Practical experience with model training, deployment, serving, monitoring, and iteration in cloud environments such as Amazon SageMaker.
• Strong understanding of machine learning methods, their real-world applications, limitations, and AI safety considerations including bias, privacy, and PII.
• Ability to write clean, maintainable, production-ready code using Git and software development best practices.
• Experience with CI/CD pipelines, DBT for data modelling, and Looker for BI reporting.

Nice to have

• Experience in credit risk modelling, collections optimisation, fraud detection, risk strategy, lending, payments, or financial services.
• Knowledge of model governance, explainability, fairness, affordability, or financial-services regulation.
• Experience with decision science, automated decisioning systems, decision engines, or risk strategy implementation.
• Experience applying neural networks or deep learning to risk, fraud, or decisioning problems.

Culture & Benefits

• Work in a cross-functional Data and Risk environment with product, engineering, credit, collections, fraud, and strategy partners.
• Contribute to coding standards, version control, testing, CI/CD, monitoring, workflows, and tooling.
• Apply evolving data and machine learning technologies to products serving millions of customers and thousands of merchants.