EIS is looking for a Senior Data Scientist to build machine learning models for insurance claims analysis and fraud detection. The role offers opportunities to work on advanced AI projects and grow with the expanding team in Japan.

What You'll Do

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

What We're Looking For

5+ years of hands-on experience in data science or applied ML, preferably in fraud, risk, or anomaly detection
Strong experience with XGBoost (or similar tree-based models)
Practical experience with unsupervised anomaly detection techniques
Advanced Python skills (pandas, numpy, scikit-learn, ML pipelines)
Experience working with large tabular datasets in production environments
Strong SQL skills; experience querying and modeling data in Snowflake
Familiarity with AWS (S3, IAM, compute services; SageMaker a plus)
Fluent Japanese and English (spoken and written)
Comfortable working with partially labeled, noisy, or evolving datasets

Nice to Have

Experience in insurance claims, auto insurance, or financial fraud
Experience combining supervised + unsupervised signals into composite risk scores
Knowledge of model explainability (SHAP, feature importance analysis)
Familiarity with data pipelines (DBT, ELT workflows)
Experience supporting regulated or compliance-sensitive environments
Prior work with bias/fairness analysis in ML models

Technical Stack

Python, pandas, numpy, scikit-learn, XGBoost
SQL, Snowflake
AWS, S3, IAM, SageMaker

Team & Environment

Operate in a Scaled Agile environment, diverse, multicultural and cross-functional teams.

Benefits & Compensation

Work with top talent and great colleagues who are industry and technology experts
Operate in a Scaled Agile environment, diverse, multicultural and cross-functional teams
We are a global and modern software product company building world-class Enterprise InsurTech Product powered by leading-edge technologies (microservices, reactive, cloud, continuous delivery)
We offer freedom - build from building your career path through development programs and exciting global mobility opportunities (we have a remote and global culture)
We work with the newest Apple Macbooks