About the job

You will be one of the first dedicated data hires at InCycling: a Berlin-based startup building AI-driven infrastructure for surplus chemical trading in a highly regulated industry. This role starts in cheminformatics and chemical data science, but as the platform grows, you'll have the opportunity to expand to Python software engineering, machine learning engineering, or data engineering, if it suits your interests.

Tasks

In this role you will:

• Build Python-based data pipelines to work with complex chemical data;
• Incorporate into our workflow public databases, pre-trained domain-specific models, and OSS tools (e.g. ChemBERTa, RDKit, DeepChem);
• Develop semantic embeddings; create augmented and synthetic chemical data; perform analysis, classification, and interpretation of chemical records;
• Parse and extract structured data from unstructured sources: proprietary databases, industrial documents, regulatory texts, process descriptions;
• Use AI tools (we use Claude Code) to structure and accelerate your work;
• Contribute to design discussions, code reviews, and retrospectives. We are a team of six, your opinions will matter.

Requirements

You should have:

• A degree in chemistry, cheminformatics, or a closely related field (e.g. biology with a strong foundation in chemistry);
• Deep interest in Python (job experience is ideal, but coursework and personal projects could suffice);
• Knowledge of data science and machine learning (academic research experience counts);
• Strong analytical skills and a love for problem-solving;
• Strong communication skills, kindness, directness, and persistence.

Our working language is English, and we work 3/2 hybrid in Berlin.

The following skills (or a strong interest in learning them) are a plus:

• Engineering mindset: version control, testing, linting, a care for code quality;
• Interest in modern, lightweight tech stacks, e.g. uv, Polars, dlt, DuckDB;
• Knowledge of adjacent data fields: Data Engineering, MLOps, DevOps, cloud technologies;
• Knowledge of adjacent fields: pharmacology, biotechnology, physics, product supply;
• Interest in using agentic workflows for data: knowledge bases, stateful agents, vector databases.

Benefits

We offer:

• The opportunity to build a digital platform from the ground up, influencing key architectural and engineering decisions;
• A small, highly collaborative team, fast decision-making. Direct mentorship from experienced data scientists and engineers (you won’t be learning from docs alone!), direct access to founders.
• A mission-driven product contributing to the circular economy by reducing waste and CO₂ emissions in the chemical industry.
• A pragmatic engineering culture with modern tools, including AI-assisted development.
• A flexible hybrid working setup and a culture built on ownership, collaboration, and continuous learning.
• Starting as a 12-month engagement tied to our funding cycle, with every intention of building a long-term journey together as InCycling grows.
• A starting salary in the €60-70k range
• Urban Sports Club membership as a company health benefit.
• Access to corporate employee benefits platforms with regularly changing monthly offers and discounts.

About InCycling:

Every year €26 billion worth of perfectly usable chemical raw materials are getting destroyed, generating millions of tonnes of avoidable CO₂ emissions and hazardous waste, not because they lack value, but because no system exists to capture it. InCycling is changing that. We are building a cloud-native AI-driven B2B platform with deep SAP integration, automated regulatory compliance workflows, and ML-based supply-vs-demand matching for scalable surplus chemical management. We are live with our first global DAX-tier enterprise pilot across 7 production sites in 4 countries and 3 continents with over 500 tonnes of surplus chemicals already in our deal pipeline. You would be joining at the moment where the foundation you build will define how the platform scales.

We welcome candidates from all backgrounds, who believe in our mission. If you do not match 100% of the requirements, but believe you can do the job, please apply: perfect candidates do not exist, but wonderful colleagues do.