Global Commodity Sourcing Data Scientist

Atmospheric AI; Greater London; Hybrid; GBP 75,000 - 110,000; Full time

Job summary

Atmospheric AI Limited is building quantitative models to infer supplier networks, origins, and flows from incomplete evidence in a 16 trillion-dollar materials economy. You’ll design probabilistic, network-based approaches that respect physical constraints and deliver traceable results that scale across suppliers and facilities.

You’ll own the sourcing model, product-origin inference, and the evidence layer, choose appropriate validation strategies, and ship production-grade Python modules with

Qualifications

• Strong background in probabilistic modelling and network analysis.
• Experience with validation under limited ground truth.
• Ability to reason about mass balance, capacity and transport.
• Proven production-grade Python development with tests and documentation.

Responsibilities

• Design and implement sourcing models to infer suppliers and flows.
• Create product-origin inferences from finished products to facilities and converters.
• Ingest and reconcile production, capacity, trade and shipment data for the evidence layer.
• Choose validation strategies and ensure credible tests for mostly unobserved targets.
• Deliver reproducible Python modules maintained by the broader team.

Skills

Network modelling

Probabilistic modelling

Validation mindset

Physical-system reasoning

Production-quality Python

Education

Master's degree in a quantitative field

PhD in a quantitative field

Tools

Python

PostgreSQL

Dagster

AWS

Job description

Atmospheric AI Limited is building quantitative models to infer supplier networks, origins, and flows from incomplete evidence in a 16 trillion-dollar materials economy. You’ll design probabilistic, network-based approaches that respect physical constraints and deliver traceable results that scale across suppliers and facilities.

You’ll own the sourcing model, product-origin inference, and the evidence layer, choose appropriate validation strategies, and ship production-grade Python modules with