ESG Data Scientist (All Genders)

About the role

As an ESG Data Scientist at carbmee you will design and implement machine-learning models that turn fragmented customer and supplier data into reliable corporate, supply-chain and product carbon footprints. The role sits at the intersection of data science and carbon accounting, helping clients move from rough estimates to defensible emissions calculations.

Key responsibilities

• Develop and fine-tune ML models and LLM-driven workflows to improve emissions data coverage and accuracy at scale.
• Build logic to handle real-world data gaps such as missing weights, inconsistent units, and unmapped commodities.
• Integrate LLM-assisted data mapping (using Claude) and create validation guardrails to prevent hallucinated mappings.
• Design models that comply with the GHG Protocol scope structure, especially the Scope 3.1/3.4 split for supply-chain footprints.
• Analyse model performance across different industry deployments and iterate quickly.
• Translate model outputs and confidence levels into clear language for sustainability and technical teams.
• Collaborate with product engineering to embed successful models into the core EIS™ platform.

Required profile

• Strong quantitative background in statistics, applied machine learning or data science.
• Hands-on experience building models, either from academic projects, internships or early-career work.
• Comfort working with ambiguous, incomplete data and a habit of flagging limitations.
• Familiarity with GHG Protocol or carbon accounting concepts is a plus.

Required skills

• Python programming
• SQL for enterprise data manipulation
• Machine learning and statistical modelling
• LLM-based workflow design and prompt engineering