Applied ML Engineer/Data Scientist

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

About grape:

Our entrepreneurial spirit drives us in what we do and what we are passionate about. Our mission is to become the premier health partner in the insurance business. Recognising the need for innovation in the traditional employee insurance model we are set to revolutionize the employee insurance industry. We help companies support people before and after they get sick and to make more of their time. Our insurance solutions reduce monotonous and manual work for employees, doctors, HR & payroll managers. As we strive to lead a technology-driven transformation, we put employees first.

At grape we pride ourselves on our people and our culture. We continue to grow fast and that's where you come in. Our team is dynamic, diverse and full of creative ideas. Want to join? We can't wait to get to know you!

Our role

Our data and tech give us an edge in risk assessment, pricing and premium calculation. This role turns that into accurate models that support our carrier partners and our growth, builds tuned models where they beat rules and off-the-shelf LLMs in claims (triage, intervention, return-to-work, fraud), and responsibly explores how data can help employees stay healthy through grape Care.

We're looking for an applied ML engineer / data scientist to build predictive models from the ground up on real, regulated insurance data. You'll own problems end to end, from effective-dated records to production, in a domain where correctness and responsible use genuinely matter.

Starting points include pricing and risk, portfolio monitoring, claims and payment patterns, triage and return-to-work, absence, and fraud detection. grape sits on a rich dataset, though, and you'll help find the next high-value use cases. You're candid about what the data can support, and you propose simpler approaches when they're the right answer.

This is not an LLM role. LLM and agent workflows and document extraction sit with our engineering teams, and you'll use their outputs as features.

What you'll do:

• 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.

Platform and MLOps

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.

Shape the problem space

• 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.

Responsible & explainable by design

• Build privacy, fairness, and explainability into every model that touches personal or health data, working with compliance and legal on nDSG/GDPR boundaries.


• Models support decisions; accountable owners make them. Pricing, reserving, and claims work requires licensed‑actuary sign-off, and any automation requires governance approval first.

Collaborate

Explain model behavior and limits to underwriting, claims, actuarial, and compliance teams.

What you'll need:

• 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.

Big plus:

• Insurance, actuarial, or risk modeling (pricing, reserving, loss ratios).


• Health, disability, or absence data, especially Swiss insurance (KTG, UVG); fraud detection in claims.


• Regulated special‑category data under nDSG/GDPR, or model risk management.


• Google Cloud, including security‑restricted deployments.


• German language skills.

What we offer in return:

• Empowering workplace: Join a passionate team and an empowering workplace where you are given the ownership, tools, and support to make decisions and take initiative. Together we grow and build innovative solutions.


• Hybrid setup: Enjoy the flexibility of our hybrid work schedule, with the freedom to work remotely or from our office in Zurich. On Thursdays, our team day, we meet together in our Zurich office.


• Home office stipend: To e