The fashion industry is responsible for 5–10% of global greenhouse gas (GHG) emissions. As pressure for climate action grows, more companies are turning to carbon management tools. But most platforms are generic, lacking the depth needed for driving real change in fashion.

Carbonfact is the Environmental Intelligence Platform built specifically for the textile and fashion industry. We turn messy, real-world business data into environmental intelligence — enabling any fashion company to measure, model, reduce and report on its impact with confidence.

We’ve raised $17M from top-tier investors including Alven, Headline, and Y Combinator, and we’re already trusted by leading brands like Carhartt, GANNI, On, Allbirds, Armedangels, Jack Wolfskin, and many more.

Data Scientist at Carbonfact

Our platform is organized around four pillars: Collect customer data through custom connectors, Measure impact from a single process to an entire brand, Reduce footprint through simulation tools, and Report with audit-ready, regulation-compliant outputs.

As a Data Scientist, you sit at the heart of the Collect and Measure pillars. Fashion brands send us bills of materials, catalogs, purchase orders — in different formats, with gaps, inconsistencies, and ambiguities. You make that data flow: parsing, normalizing, enriching, filling the gaps, flagging anomalies, and connecting it to our LCA engine so brands can measure and reduce their footprint.

You'll leverage AI as a power tool in your daily work to keep our connectors lean. What matters is your judgment on data quality, ability to write production-grade code, and instinct for spotting what's wrong in a dataset before it reaches a customer.

You'll work closely with customer data, joining client calls (a couple per week) and visiting brands onsite once or twice a year. Think of it as applied data work with a direct line to impact — every dataset you clean and every anomaly you catch, translate into a more accurate measurement of environmental footprint for a real product on a real shelf.

Carbonfact's product is a Data Platform so you work is at the core of our value proposition.

What you will do

Parse, normalize, and transform raw business data (BOMs, catalogs, purchase orders) into Carbonfact's internal data models
Build and improve automated gap-filling and anomaly detection on customer datasets
Develop analytics on customer data for both internal use and external presentation to brands
Contribute to building and improving the Carbonfact platform
Partner with Customer Operations and Science teams to deliver value to customers
Handle technical integration discussions with customers

What you won't do

Train machine learning models. We use frontier AI models extensively — we don't build them. Your value is in judgment and domain expertise, not gradient descent.
Build heavy ETL pipelines. We maintain light connectors to our clients' diverse IT systems and spreadsheets. No Airflow DAGs, no Spark clusters, no data warehouse plumbing.
Work in isolation. We build analytics to enable real decisions by the customers. You will own the delivery of data insights to help customers meet their decarbonization goals.

Who you are

You have 2+ years of professional experience
Strong applied Python skills — you can build and improve our internal tooling, not just use it
You are used to (or getting used to) leveraging agentic engineering to build a sustainable and healthy codebase
Solid software engineering fundamentals: clean code, unit tests, version control — we have a high bar for code quality
Comfortable with NLP basics: regex, typo handling, normalization, fuzzy matching
SQL skills and curiosity for analytics engineering
You communicate well in English and can discuss technical concepts with customers
You're excited by heterogeneous, real-world data — the messier, the more interesting
Bonus: data engineering experience or familiarity with environmental data

What we offer

A transparent, collaborative and high-agency culture rooted in Carbonfact's principles here.
AI-first tooling: generous access to frontier AI models, Claude Code, GitHub Copilot
MacBook, headset, and all the modern essentials
100% coverage of premium health insurance with Alan
Fitness and commuter benefits
Annual learning budget to support professional development
Team retreats twice a year and occasional onsite visits to brands (see the real-world impact of your work)
Transparent compensation framework with level-based salary and equity; promotions tied to impact (salary range for this role: €55k–€74K depending on level)
Compelling equity package with employee-friendly exercise rights