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Tide is a fintech platform serving over 2 million SMEs globally with business banking, invoicing, and accounting solutions. The company operates across the UK, India, Germany, and France with 2,800+ employees and has raised over $300 million in funding.
As Staff Data Scientist for Fraud & Risk, you will be a hands-on individual contributor leading the design and development of advanced machine learning models to detect and mitigate fraud at global scale. This is a technical leadership role where you'll act as the subject matter expert (SME) for the fraud and risk team, setting technical standards and mentoring peers through example.
Key responsibilities include:
- Design and deploy production-grade classical ML models (XGBoost, LightGBM, ensemble methods) for fraud detection, risk assessment, and anomaly detection across imbalanced datasets
- Build and scale GenAI and agentic AI workflows (LangGraph, AWS Bedrock, GCP) to automate alert handling and accelerate fraud investigations
- Identify, measure, and resolve data drift and concept drift in production pipelines to maintain continuous model performance
- Collaborate with ML engineers on CI/CD pipelines, model tracking, and observability
- Partner with Data Engineering on feature engineering and real-time/batch fraud decisioning
- Translate complex fraud typologies and business requirements into rigorous mathematical formulations
- Build and track business-impact metrics, feeding insights back to Product and Business teams
- Present complex findings to non-technical stakeholders with strong storytelling
You'll work closely with Business Teams, Product Managers, Data Governance, Analysts, Scientists, and Data Engineers to deliver company and product OKRs.
