Applied ML Engineer & Data Scientist - Hybrid (Zurich)

Grape-Insurance; Zürich; Hybrid; CHF 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work schedule

CHF 200 home office budget

Internet allowance

Learning budget CHF 1500 per year

5 weeks paid vacation

Job summary

grape is seeking an applied ML engineer / data scientist to build predictive models from real, regulated insurance data. You will own problems end to end, from effective-dated records to production, in a domain where correctness and responsible use matter.

Starting points include pricing, risk, portfolio monitoring, claims patterns, triage, and fraud detection. You’ll work with our cloud platform under strict security and data-residency requirements for health data.

Qualifications

• Strong applied ML and statistics: supervised and unsupervised learning, anomaly detection, time-series, and survival/duration modeling.
• Proven experience building models on real-world, relational, effective-dated data, including imbalanced outcomes.
• Fluent Python (pandas, scikit-learn, a modern ML stack) and strong SQL.
• Solid grasp of validation, calibration, and explainability (e.g. SHAP).
• Production MLOps: experiment tracking, versioning, and drift monitoring.

Responsibilities

• Build and validate models end-to-end across pricing, risk, portfolio monitoring, claims, and fraud detection in a regulated insurance domain.
• Own the full model lifecycle: feature engineering, training, evaluation, explainability, deployment, and drift monitoring.
• Backtest on historical data and run in shadow mode with human decision-makers, documenting disagreements.
• Produce audit-ready validation and governance documents for actuarial, compliance, and carrier partners.
• Collaborate with engineering on MLOps, cloud deployment, and integration while maintaining data residency requirements.
• Explain model behavior and limits to underwriting, claims, actuarial, and compliance teams.

Skills

Applied ML

Python

SQL

Model deployment

Explainability

MLOps

Data privacy

English

Tools

pandas

scikit-learn

MLflow

Git

Job description

grape is seeking an applied ML engineer / data scientist to build predictive models from real, regulated insurance data. You will own problems end to end, from effective-dated records to production, in a domain where correctness and responsible use matter.

Starting points include pricing, risk, portfolio monitoring, claims patterns, triage, and fraud detection. You’ll work with our cloud platform under strict security and data-residency requirements for health data.