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
Work Mode
This role is global, with a location in Japan.
EIS is an equal opportunity employer.
