Hybrid ML Engineer & Data Scientist in Health Analytics

Grape Health; Zürich; Hybrid; CHF 120,000 - 190,000; Full time

Job summary

Grape Health in Zürich is seeking an applied ML engineer / data scientist to build predictive models from regulated health data. You will own end-to-end model development, from data handling and feature engineering to production deployment with strict privacy and residency rules.

The role emphasizes responsible AI, model governance, and collaboration with underwriting, claims, actuarial and compliance teams. Fluency in English and strong Python/SQL are required.

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.
• Sound judgment on data privacy and modelling.
• Fluent English.
• Bias for action, ownership, and clear cross-functional communication.

Responsibilities

• Build and validate models.
• Own the full model lifecycle: feature engineering, training, evaluation, explainability, deployment, and drift monitoring.
• Backtest on the historical book and run models in shadow mode alongside human decision-makers, reporting where and why they disagree.
• Produce audit-ready validation and governance documentation that actuarial, compliance, and carrier partners can review.
• Deploy models on grape's cloud platform within strict security and data-residency requirements for health data.
• Set up experiment tracking, model versioning, and production monitoring, partnering with engineering on integration.
• Identify and prioritize high-value use cases as you explore the dataset, rather than waiting to be handed problems.
• Turn ambiguous business questions into well-defined, measurable modelling.
• Build privacy, fairness, and explainability into every model that touches personal or health data, working with compliance and legal on nDSG/GDPR boundaries.
• Collaborate: Explain model behavior and limits to underwriting, claims, actuarial, and compliance teams.

Skills

Applied ML & stats

Python (pandas, scikit-learn)

SQL

Model validation & explainability (SHa

Production MLOps

Data privacy & modelling judgement

Fluent English

Cross-functional communication

Tools

Google Cloud

Job description

Grape Health in Zürich is seeking an applied ML engineer / data scientist to build predictive models from regulated health data. You will own end-to-end model development, from data handling and feature engineering to production deployment with strict privacy and residency rules.

The role emphasizes responsible AI, model governance, and collaboration with underwriting, claims, actuarial and compliance teams. Fluency in English and strong Python/SQL are required.