Applied ML Engineer/Data Scientist

grape insurance AG; Zürich; Hybrid; CHF 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work

CHF 200 home office budget

Internet allowance

Flexible hours

Learning budget 1500 CHF

Well-being budget

Team events

Parental leave

Digital nomad

Job summary

grape insurance AG hires an applied ML engineer / data scientist to build predictive models on real, regulated insurance data. You will own end-to-end problems from effective-dated records to production, with a focus on correctness, privacy and governance.

Starting points include pricing, risk, claims, fraud detection and absence data. You’ll work with our data and engineering teams in a hybrid Zurich setting, using Python, SQL, and modern ML tools to drive value responsibly.

Qualifications

• Strong applied ML and statistics across supervised/unsupervised learning.
• Experience with real-world, relational, effective-dated data.
• Production MLOps: tracking, versioning, drift monitoring.
• Fluent English; Swiss market familiarity a plus.

Responsibilities

• Build and validate predictive models end to end, from features to deployment.
• Backtest on historical data and run in shadow mode with governance.
• Set up platform tooling for monitoring and experiment tracking.
• Ensure privacy, fairness, and explainability in models.
• Collaborate with underwriting, claims, actuarial and compliance teams.

Skills

Python

SQL

MLOps

Time-series

SHAP

Explainability

Tools

Pandas

scikit-learn

Google Cloud

MLflow

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!

Your 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.

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 enable our hybrid setup, we allocate a one-off CHF 200.- home office budget to help you set up your dream workspace in the comfort of your own home.
• Internet allowance: We give you a monthly internet allowance to help cover your internet costs at home and enable you to work seamlessly from your home office.
• Flexible working time: You can distribute your working hours the way you want to.
• Learning budget: We support you in improving your skills, acquiring new knowledge and advancing your career. We also allocate a CHF 1’500.- budget annually
• Well-being: Work-life balance is important to us and we foster a supportive work environment. We support you with mental health sessions with our partner Aepsy.
• Team events: Every month we gather for fun and engaging events where we can connect and bond with our colleagues and our teams.
• Team retreats: Once a year we travel somewhere nice to bond with our colleagues.
• Travel and commute: We will cover your SBB half fare card, so we can spend some time together in the office.
• Paid vacation: You get 5 weeks (25 days) of paid vacation every year and 2 additional days of paid vacation to compensate for canton‑related holidays, religious holidays, birthdays, etc. The total amount of paid vacation days per year is 27 days.
• Wellness budget: Health and well-being looks different for everyone. You can optimize your wellness budget to best fit your needs.
• Parental and adoptive leave: We offer additional time off to support employees who are balancing family and work time in the most crucial stages of family life.
• Digital nomad: For every 3 years of employment, you can work for an extended period of time from anywhere in the world (location subject to review).

Our recruitment process

We place particular emphasis on four areas when making our selection decision: professional and technical skills, mindset & values. We evaluate these through the following steps:

Step 1 - Your motivation

To get to know you better, we invite you to a 30-minute motivational interview with our Talent Acquisition Team.

Step 2 - Role fit

In the first interview, we assess how well your qualifications and skills align with the role requirements. This discussion will take place with your Hiring Manager.

Step 3 - Case study

For roles with higher technical demands, we will invite you to complete a case study or technical assessment. This allows us to better evaluate your specific skills and problem-solving abilities.

Step 4 - Our culture connect

Throughout the interview process, you’ll gain insight into our company culture, and we’ll explore how your values and mindset align with our ways of working. The final interview will offer a deeper look into this connection, along with broader discussions about the role and our vision.

Our diverse experiences, perspectives, and voices make us the company we are, and we would love to learn more about you!

Health is our foundation. Innovation drives our mission. Together, we’re building the future of employee insurance.

At grape, collaboration isn’t just a buzzword, it’s the foundation of how we work, live, and succeed. From bold brainstorms and lessons learned, we overcome challenges side by side. Our time together is our greatest asset. Disrupting an industry that truly needs change takes courage, creativity, and team spirit and that’s exactly what defines us.

With well-being at the core, we foster an environment where ideas and people thrive. Your growth matters as much as our mission. Whether it’s supporting your self‑care, developing new solutions, or redefining how health and insurance connect, we grow together.

Our values guide us every step of the way: keepingHealth at Heart,_Invent_ingTogether, and always striving toEmbrace Clarity.