• Innovate and improve machine learning models • Design, develop, and deploy machine learning models to solve problems • Partner with business leaders and technical experts to develop new data sources • Improve modeling methodology and apply models with sound risk management

• Innovate and improve machine learning models • Design, develop, and deploy machine learning models • Partner with business leaders and technical experts to develop new data sources • Improve modeling methodology and apply models with sound risk management

• Improve existing supervised fraud detection models (primarily XGBoost) through feature engineering, tuning, and evaluation • Design and implement unsupervised and semi-supervised anomaly detection models (e.g., ECOD, Isolation Forest, clustering-based approaches) • Explore ensemble strategies combining anomaly signals, supervised scores, and business rules • Analyze fraud patterns in FNOL (First Notice of Loss) and downstream claim lifecycle data • Develop new model concepts for emerging fraud patterns (temporal anomalies, behavior shifts, structural inconsistencies) • Evaluate bias, stability, and confidence of fraud scores and support threshold calibration • Partner with engineering to productionize models in AWS + Snowflake environments • Support customer-facing analysis and explanations, including Japanese-language discussions when needed • Document findings, assumptions, and tradeoffs clearly for technical and non-technical audiences

• Responsible for designing and managing search recommendation services and models • Knowledge of machine learning powered personalization methods, design patterns and tools • In-depth understanding of Recommendation systems with the business domain knowledge to take the impact of AI product to the next level • Drive the development of core modules with data-driven strategy to maximize the impact • Identify the needs and opportunities, define success metrics with Business and Product management teams