Context

We are Sustaain, a data-for-sustainability startup building a sustainability intelligence platform for complex supply chains such as coffee and cocoa with an initial focus on data management for European Union Deforestation Regulation (EUDR) compliance and risk management.

Over the next 6 months, we’re shipping production-grade compliance workflows: portal UX + APIs, traceability + geospatial services, auditability, and document ingestion & automation as a first-class module. This sits at the heart of the unified compliance operations vision we’re building with large commodity traders.

Role Overview

We are looking for a Data Scientist to industrialize how we compute, validate, and explain compliance-grade risk and integrity signals — from deforestation risk to supplier/country risk, to consistency checks between traceability, geospatial data, and evidence.

You will report to our Lead Data & Delivery Lead (already in the team) and work closely with the engineering team and delivery team to bring models and scoring logic into robust production systems: evaluation, monitoring, auditability, and tight integration with data models and reviewer workflows.

Responsibilities

• Build and ship risk and validation logic (plot/farm/lot level) using geospatial and supply-chain signals; make outputs explainable and reviewable.
• Define integrity checks across traceability ↔ polygons ↔ deliveries ↔ evidence, and turn them into deterministic, testable rules with measurable coverage.
• Own evaluation: gold datasets, benchmarks, regression tests, quality gates, and human-in-the-loop review workflows.
• Implement production guardrails: documentation of assumptions, versioning, audit trails for scoring changes, and traceability of outputs (who/what/when/why).
• Collaborate with product and data teams to translate compliance requirements into “shippable” workflows (review screens, evidence packs, audit trails, operational dashboards).
• Partner with engineering to operationalize models (APIs/batch), monitor drift and failures, and iterate fast based on user feedback.

Required Skills & Experience

• Strong applied DS fundamentals (Python), production mindset, and a track record of shipping.
• Hands-on experience building scoring/validation systems on messy real-world data (not just notebooks).
• Solid understanding of data modeling basics (schemas, validation, pipelines, observability) and how models fit into product workflows.
• Comfort with ambiguity: you can define the problem, measure it, and iterate fast.
• You care about correctness and accountability (this is compliance, not content).

Nice-to-Have