Lead Data Scientist - Fraud Risk

Role overview

Key responsibilities

• Lead development and deployment of machine-learning models and data-science solutions across markets.
• Analyze customer, transaction, and product data for trends, patterns, risks, and opportunities.
• Design experiments to evaluate product changes, decision systems, and customer-experience improvements.
• Build scalable models for prioritization, personalization, risk management, and operational decisions.
• Translate business requirements into data-science solutions with cross-functional teams.
• Develop production-grade data pipelines, algorithms, and tools.
• Monitor advances in data science, machine learning, and payment-fraud prevention.

Candidate requirements

• Proven end-to-end model deployment experience, from preprocessing and feature engineering through evaluation and monitoring.
• Strong Python skills; Git collaboration and code-review experience; ability to read code, especially Java.
• Experience collaborating with engineering on services and working with large datasets and processing technologies such as Hadoop, Spark, or SQL.
• Familiarity with anomaly detection, supervised and unsupervised learning, real-time analysis, and statistical analysis.
• Strong presentation, communication, product, independent-work, and problem-solving skills.

Advantageous experience

• MLOps tools such as Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, and CI/CD are helpful but not essential.
• Fraud-domain experience and knowledge of fraud-detection techniques are helpful but not essential.
• Production deployment of LLM-based solutions is helpful but not essential.

Salary

• Salary is GBP 90,500–127,000 annually.

Workplace

• Hybrid working model with flexibility to work from home or overseas.

Benefits

• Stock options in a profitable company.
• Annual personal development budget for books, courses, or conferences.
• Visa and relocation support.