Applied ML Engineer/Data Scientist

Location: Zürich, Switzerland; hybrid setup with a regular Thursday team day in the Zurich office

Company

hirify.global builds employee insurance solutions using data and technology for risk assessment, pricing, claims, and employee health.

What you will do

• Own the full lifecycle of predictive models, including feature engineering, training, evaluation, explainability, deployment, and drift monitoring.
• Build models for pricing, risk, portfolio monitoring, claims, payment patterns, triage, return-to-work, absence, and fraud detection.
• Backtest models on historical insurance data and run them in shadow mode alongside human decision-makers, explaining disagreements.
• Deploy models on the cloud platform under strict security and health-data residency requirements; establish experiment tracking, versioning, and production monitoring.
• Identify high-value use cases and translate ambiguous business questions into measurable modelling problems.
• Work with underwriting, claims, actuarial, engineering, compliance, and legal teams on model behavior, limitations, governance, privacy, fairness, and explainability.

Requirements

• Strong applied machine learning and statistics skills, including supervised and unsupervised learning, anomaly detection, time series, and survival/duration modelling.
• Experience building models on real-world relational and effective-dated data, including imbalanced outcomes.
• Fluent Python with pandas, scikit-learn, and a modern machine learning stack, plus strong SQL.
• Solid understanding of validation, calibration, and explainability, such as SHAP.
• Production MLOps experience with experiment tracking, model versioning, and drift monitoring.
• Sound judgment regarding data privacy and modelling, with awareness of responsible model use.
• Fluent English required.
• Bias for action, ownership, and clear cross-functional communication.

Nice to have

• Insurance, actuarial, or risk modelling experience, including pricing, reserving, or loss ratios.
• Health, disability, or absence data experience, especially Swiss insurance such as KTG or UVG; claims fraud detection experience.
• Experience with regulated special-category data under nDSG/GDPR or model risk management.
• Google Cloud experience, including security-restricted deployments.
• German language skills.

Culture & Benefits

• Hybrid work with remote flexibility and a Zurich office team day every Thursday.
• Flexible working hours, a CHF 200 one-off home-office budget, and a monthly internet allowance.
• CHF 1,500 annual learning budget, wellness budget, and mental-health sessions through Aepsy.
• 27 paid vacation days annually, plus parental and adoptive leave.
• Monthly team events, annual team retreats, and SBB half-fare card coverage.
• After every three years of employment, the option to work for an extended period from anywhere in the world, subject to review.

Hiring process

• Four steps: motivation, role fit, case study, and culture connect.