ESG Software Tester — GHG Scope 3 & ESG Data Specialist

ESG Software Tester — GHG Scope 3 & ESG Data Specialist AI-Assisted Testing Model · v2 Platform: Upwork / Freelancer Working Model: Autonomous · AI Assisted Duration: 6–7 weeks (extendable) Commitment: 20–25 hours/week Work Style: Remote · Async-first · Self-directed ABOUT THE PROJECT We are building an ESG DataHub — a cloud-based ESG data management and reporting platform for mid-to-large enterprises. The platform covers GHG emissions tracking (Scope 1, 2, and 3), ESG data collection via configurable questionnaires, automated calculations, and regulatory reporting across GRI, BRSR, TCFD, and ISSB S1/S2. We need a freelance ESG domain expert who will work as the primary tester for the platform - self-directed, working alongside AI tools that handle test case generation and calculation verification logic, while you provide the domain judgment, real-world validation, and test execution that AI cannot replace. HOW THE WORKING MODEL OPERATES AI tools generate the test case library, calculation verification scripts, emission factor checklists, and framework compliance checks. Your role is to execute tests, apply real-world ESG judgment to outputs, validate methodology correctness, and make the pass/fail calls that require genuine domain expertise. You will not have an internal ESG team member alongside you day-to-day - you work autonomously and report findings directly to the technical lead weekly. WHAT YOU WILL DO - Execute AI-generated test cases against the live platform - test cases, expected calculation outputs, and framework compliance checklists are provided by AI tools; your job is to run them and apply domain judgment to every result. - Validate Scope 3 calculation outputs against GHG Protocol methodology across all relevant categories — purchased goods, upstream transport, use of sold products, financed emissions (Cat 15 PCAF), and others. - Apply real-world sanity checks — verify that outputs are not only mathematically correct but plausible and appropriate for the client sector (manufacturing or insurance). This is the judgment AI cannot make. - Validate ESG framework compliance — check that reporting outputs (GRI, BRSR, TCFD, ISSB S1/S2) have correct disclosure structure, content index accuracy, and omission handling. - Conduct emission factor audits — verify that factors used are from recognised sources (IPCC, DEFRA, IEA, PCAF), are sector-appropriate, and are current. - Document bugs and methodology discrepancies clearly — distinguishing between a software defect and a methodology/configuration error. Structured bug reports required. - Test against real client datasets with known manually-calculated Scope 3 outputs — confirm ESG Software’s results match established figures. WHAT WE NEED Non-Negotiable Requirements: GHG Scope 3 expertise — Strong, hands-on knowledge of GHG Protocol Scope 3 categories. Ability to independently verify whether a Scope 3 output is correct — not just cite the standard. Corporate ESG experience — Direct experience working with or auditing ESG data in a corporate context. Theoretical or academic knowledge alone is not sufficient. ESG framework knowledge — Working knowledge of at least one major framework: GRI, IFRS/ ISSB. Must understand disclosure structure, not just terminology. Manufacturing sector ESG — Practical experience with manufacturing ESG data: production emissions, energy intensity, supply chain Scope 3, waste, water. Autonomous working style — Ability to work independently without day-to-day internal ESG team support. Self-direction, clear documentation, and proactive communication are essential. AI tools comfort — Comfortable using AI tools for ESG analysis, calculation verification, and research. The workflow is AI-assisted; resistance to this model is a disqualifier. Written English — Fluent written English. All documentation and bug reports are in English. Sector experience — at least one of: - Manufacturing: Scope 3 Cat 1 (purchased goods — spend-based and activity-based), Cat 4 (upstream transport), Cat 11 (use of sold products), operational emissions intensity - Insurance / Financial: Scope 3 Cat 15 (financed emissions under PCAF methodology), portfolio carbon intensity, asset-level emissions attribution, data gap handling Nice to Have: - IFRS S1/S2 — climate-related financial risk disclosures for insurance/financial sector - ESG software platform experience — Watershed, Persefoni, Sweep, Sphera, or similar; especially configuration and UAT (User Acceptance Testing) - GHG verification or auditing — ISO 14064, CDP submission experience - IFRS S1/ S2, BRSR (India) or CSRD (EU) — regulatory reporting exposure - Prior use of AI tools for ESG analysis — using AI to verify calculations, cross-check emission factors, or generate compliance checklists ENGAGEMENT DETAILS Type: Freelance / Contract Duration: 6–7 weeks initial; strong potential for ongoing retainer Commitment: 20–25 hours/week (self-directed) Working model: Autonomous · AI for test case generation and calculation verification · Weekly reporting to technical lead · No internal ESG team support day-to-day Platform: Web application; no coding required — functional and domain QA only Test data: Real manufacturing client dataset with manually verified Scope 3 outputs provided as primary reference; Test cases for additional categories and edge cases Rate: Please state your hourly rate in your proposal HOW TO APPLY Include the following in your proposal. Applications without complete answers to all three questions will not be reviewed. 1. Scope 3 project experience. Describe a Scope 3 project you worked on: which categories, what data sources, what methodology, and what the output was used for. 2. Sector experience. Describe your experience with manufacturing and/or insurance sector ESG data specifically — not ESG in general. 3. Error you caught. Give one example of a significant ESG data or calculation error you identified — what it was, how you found it, and what the impact would have been if it had gone undetected. We are not looking for generalist QA testers. We need someone who knows GHG and ESG well enough to tell us when the software is wrong — even when it looks right. AI handles the test case writing; you supply the judgment. No answer = no review.