Data Scientist (Product-oriented)

Dryp builds IoT- and data-driven technology that helps utilities and cities prevent flooding and reduce environmental impact from wastewater and stormwater systems. We combine advanced sensor networks, hydrological modelling, and cloud software to enable real-time operational decision-making for utilities across the Nordics.As we scale our product and expand our capabilities, we are now strengthening our data science and ML foundation. We are looking for aProduct-orientedData Scientistto help shape the next generation of automated insights in our platform Lens.

The Role

You will play a key role in turning complex, real-world data into actionable insights for our customers. This is a highly product-facing role where you will work closely with product, hydrologists, backend engineers, and frontend developers.

You will

Drive the development of next-generation automated insights inLens

Translate complex data streams (sensors, rainfall, SCADA, GIS) into actionable outputs for operational decision-making

Build and improve models for forecasting, event detection, and smart gap filling in time series data

Define the concept of “minimum viable truth” in data: when is data good enough to support decisions?

Contribute to automating data cleaning, validation, and quality assurance processes

Experiment with AI/ML approaches to scale insights across customers and networks

Translate domain knowledge from hydraulics and wastewater systems into scalable models and features

Who You Are

We’relooking for someone who thrives at the intersection of data science, product thinking, and real-world physical systems.

You willlikely recognizeyourself in this

You take initiative and enjoy turning open-ended problems into concrete models and solutions

You are curious about the domain and motivated by understanding how water systems behave in the real world

You have an eye fordisseminatingand presentingmodel results to actionable insights for users(in cooperation with UI/UX colleagues)

You are strong in working with noisy, imperfect, and incomplete datasets

You think beyond models and care about how they create real product value

You enjoy collaborating with both technical and non-technical stakeholders

Experience That Matters

Must-haves

Solid experience in applied data science / machine learning in a productand operationalenvironment

Strong Python skills and experience working with time series dataand databases

Experience handling noisy, real-world sensor or operational dataAbility to bring data science work into production and turn it into product features

Experience from startup/scale-up or similarly fast-paced environments

Nice-to-haves

Experience with forecasting, anomaly detection, or event detection in time series

Familiarity with streaming data or event-driven architectures

Interest or experience in hydraulics, fluid dynamics, rainfall, or physical modelling

Experience with cloud platforms (AWS) and deploying ML systemson scalable infrastructure

Knowledge of data pipelines and data quality frameworks

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