Machine Learning Scientist

Design, develop, and evaluate machine learning models for fraud detection, including fraud scoring and ensemble models that combine multiple sources of evidence.

Analyze large-scale datasets to identify complex patterns, relationships, behaviors, and anomalies, developing new signals to improve fraud detection.

Define robust experimentation and evaluation methodologies, including model calibration, operating thresholds, and performance metrics under realistic production conditions.

Develop interpretable and explainable ML approaches that help internal stakeholders and customers understand fraud scores and decisions.

Explore innovative approaches to fraud decisioning, including human-in-the-loop techniques and causal methods.

Collaborate with Product and Fraud Domain Experts to translate real-world fraud challenges and production feedback into effective machine learning capabilities.

Partner with Data Engineering to transform ML prototypes into scalable, maintainable, production-ready solutions.

Contribute to model monitoring, governance, technical documentation, ML tooling, and shared best practices across the team.

A Bachelor's, Master's, or PhD degree in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field, or equivalent practical experience.

Skills and expertise typically gained through approximately 5 years of experience as a Data Scientist, Applied ML Scientist, or in a comparable role.

Strong knowledge of statistical methods, supervised machine learning, and model evaluation, particularly for highly imbalanced classification problems.

Hands-on experience in feature engineering and machine learning model development using complex behavioral, relational, or contextual data.

Strong programming and analytical skills using Python, PySpark, and SQL.

Experience working with cloud-based machine learning and data infrastructure.

A pragmatic and hands-on machine learning practitioner who focuses on solving meaningful business problems rather than adding unnecessary model complexity.

Naturally curious, with a passion for discovering patterns and relationships in data and experimenting with new approaches.

Comfortable using data and evidence to challenge assumptions and influence technical and product decisions.

Collaborative and willing to share knowledge, mentor colleagues, and contribute to continuous improvement.

A confident communicator with strong spoken and written English skills.

Comfortable collaborating with distributed teams across different offices and time zones.

Self-motivated, adaptable, and able to work autonomously while contributing positively to a team.

Deliver measurable impact: Develop and improve machine learning models that strengthen fraud detection, support more accurate decisions, and deliver tangible business value.

Turn insights into innovation: Identify meaningful patterns, behaviors, and relationships in complex datasets and translate them into effective fraud detection signals.

Build reliable, explainable ML solutions: Create models that are robust, interpretable, and evaluated against realistic production conditions.

Take ideas into production: Work closely with Data Engineering to transform promising ML experiments into scalable, maintainable solutions.

Drive continuous improvement: Use experimentation, production feedback, and emerging ML techniques to improve model performance and fraud decisioning.

Make an impact through collaboration: Partner with Product, Fraud Domain Experts, and technical teams to solve complex challenges, share knowledge, and advance ML best practices.