Climate Data Transformation and AI Specialist Full Time

Key Responsibilities

AI Enablement & Automation

• AI adoption: Serve as the team’s internal AI champion by identifying automation opportunities, testing new solutions, and encouraging the use of AI tools in daily workflows.
• Python automation: Build and maintain Python scripts to automate repetitive tasks such as data collection, transformation, quality checks, and reporting.
• LLM workflow integration: Integrate tools such as Claude, Copilot, and ChatGPT into operational processes for document extraction, content classification, and anomaly detection.
• Structured data design: Create AI-ready data collection methods that convert unstructured disclosures into structured, machine-readable formats at scale.
• Output validation: Review AI-generated outputs, understand model limitations, and ensure human oversight remains part of the process.

Data Operations & Quality Assurance

• Data lifecycle ownership: Manage end-to-end data collection cycles, including sourcing, processing, and validating climate and ESG data from company reports, vendor feeds, and public filings.
• QA framework development: Design and implement multi-layer quality assurance frameworks, rule-based checks, and anomaly detection methods to maintain data accuracy and consistency.
• Dashboard management: Develop and maintain operational dashboards, trackers, and reference systems to monitor SLAs, coverage, and progress.
• Data issue resolution: Identify and resolve data integrity issues while coordinating with vendors, analysts, and technology teams where required.

Process Improvement & Documentation

• Workflow optimization: Identify opportunities to simplify, standardize, and scale existing workflows through better tools and automation.
• Documentation: Create SOPs, process guides, and knowledge-base articles to support audit readiness and smooth knowledge transfer.
• Cross-functional contribution: Participate in working groups and cross-functional initiatives focused on expanding data coverage and improving operational capacity.

Stakeholder Collaboration

• Team partnership: Work closely with Research, Product, and Technology teams to align data requirements and translate business needs into technical solutions.
• Analyst enablement: Train and support analysts in using new tools, scripts, and AI-assisted workflows.
• Clear communication: Communicate data methodology, process updates, and query resolutions clearly to internal and external stakeholders.

Skills and Experience

Education

• Academic background: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or another quantitative field.
• Related disciplines: Degrees in Finance, Environmental Science, or Sustainability may also be considered if supported by strong technical experience.

Experience

• Relevant experience: 4–7 years of experience in data operations, data engineering, analytics, or related roles.
• ESG or climate exposure: Prior experience with ESG or climate data is a strong advantage.

Programming

• Python proficiency: Minimum 2 years of hands-on Python experience, with the ability to independently script, automate workflows, and analyse data pipelines.