Hydrology Data Scientist (100%)

Dieses Stelleninserat von Riverkin AG wurde von unserem Impact-Team für dich kuratiert.

Warum ist das ein Futurejob? Du entwickelst ein lokales Sturzflut-Modell aus Sensor- und Wetterdaten und gestaltest damit eine Technologie, die Einsatzkräfte und Gemeinden früher vor gefährlichen Hochwasserereignissen warnen kann.

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About the Role

Riverkin is a VC-backed Swiss startup looking for a star hydrologist to join the team. Flash floods threaten thousands of people and cause millions in damage each year but trustworthy alerts are not possible to come by. This is a real, under-served problem where today’s forecasts break down, leaving first responders and municipalities exposed.

What You’ll Do

Build the first flash flood ML model

• Define and build the first version of the flash flood ML model, using sensor data we deploy ourselves and public data sources.
• You implement models, manage big data sets and monitor performance.
• You work with software engineer(s) to embed models into a scalable piece of software.
• You are the architect and choose the models, the sensors, and the architecture as we go.

Translate Business and Customer Needs into a Technical Roadmap

• You’ll partner directly with the founders to pull detailed needs out of clients and find the sweet spot between those needs and technical feasibility.
• You keep track of new developments in nowcasting and flood forecasting tools and techniques and own an ambitious technical vision.
• You find the balance between scalable and pragmatic solutions, serving customers from day 1 while working towards a long term strategy.

Represent the team on all fronts

• You’re comfortable doing a sensor install on a Tuesday and a customer pitch on a Wednesday.
• You are supportive within the team, while standing up for what you believe is best for the customer or company strategy.
• You are an opinionated contributor to key strategic decisions in the company.

Create Clarity and Momentum

• Bring structure to ambiguous problems and drive them to execution.
• Make decisions and move forward without waiting for perfect information.

Who You Are

• You’re excited to solve flash flooding at the local scale.
• You want to build a company and you’re hungry to act like a founder from day one.
• You want to have skin in the game.
• You enjoy being a hands-on.
• Can take messy inputs and turn them into clear technical direction.
• Can move fast, but with enough structure to avoid future pain.
• Care deeply about building something that actually works in the real world.
• An excellent communicator, with strong written and verbal skills.

Technical Requirements

• Strong experience applying ML tools (e.g. LSTM) to hydrological problems (e.g. flooding).
• Experience in bias correction of weather radar.
• MSc or PhD in hydrology, water resources management, atmospheric sciences, or equivalent.
• Experience working with real-world data systems (gauges, GIS, satellite imagery, etc.).

Nice to Have

• Previous founder or early startup experience.

Role Details

• Commitment: 100% FTE.
• Compensation: Commensurate with experience + equity.
• Growth: this is a key early hire with the opportunity to build and lead Riverkin’s flash-flood modelling function.
• Location: 4 days per week in Zurich office.

About Riverkin

We are a well-capitalized Swiss startup building real-time sensing and data infrastructure for rivers. Our own field-deployed sensors collect live water-level and sediment data, while our platform turns that data into decision-ready insights for customers. We work across two core use cases: hydropower, where better sediment data can support flushing, asset protection and compliance; and flash-flood risk, where local river data can help first responders and municipalities understand what is happening before conditions become critical. We are a small, hands-on team working across hydrology, hardware, field engineering, software and market development.

To apply email a brief motivation and CV to info@riverkin.com

Themen: Data Science, Hochwasserschutz, Hydrologie, Klimarisiken, Machine Learning, Sensordaten, Sturzflutwarnung, Wasserressourcen