Data Scientist (Fintech)

Location: London, United Kingdom — office-based hybrid work

Company

hirify.global provides payment, multicurrency account, e-commerce, and commerce solutions for businesses.

What you will do

• Build and maintain production machine learning models for customer lifetime value, churn, propensity to buy, and next most likely product prediction.
• Develop fraud detection models for transaction classification, merchant behavior anomalies, and new-account risk scoring.
• Contribute model outputs to next-best-action decisioning across Sales, Marketing, and product touchpoints.
• Design end-to-end ML pipelines covering data ingestion, feature engineering, training, evaluation, and deployment.
• Run A/B experiments, uplift tests, and multi-armed bandit evaluations to measure incremental impact.
• Monitor production models for performance degradation, data drift, and data quality issues, while contributing to LLM-powered agentic workflows and documentation.

Requirements

• 3–5 years of commercial applied data science, machine learning, or statistical modeling experience with production models.
• Strong Python skills with pandas, NumPy, scikit-learn, XGBoost or LightGBM, and PyTorch or TensorFlow.
• Knowledge of supervised and unsupervised learning, including classification, regression, clustering, survival analysis, and time-series modeling.
• Experience building production CLTV, churn, fraud detection, propensity, or uplift models.
• Proficiency in SQL and experience with large-scale structured and semi-structured data; GCP and BigQuery are strongly preferred.
• Knowledge of ML experiment tracking, model serving, LLM APIs, agentic AI frameworks, and responsible AI methods.

Culture & Benefits

• Annual salary reviews, promotions, and performance bonuses.
• Employer pension contribution of 9%.
• Health insurance, dental insurance, and group life assurance.
• hirify.global Academy, unlimited LinkedIn Learning access, and an annual training and development budget.
• Teambuilding, social activities, and multinational professional networks.