The Geospatial Data Team manages collection operations, data quality, and best practices for Climate and ESG geospatial data. The team drives data acquisition, vendor coordination, QA engineering, process automation, and research enablement—serving as the operational backbone for MSCI’s geospatial data products.

• Geospatial Data Acquisition: Gatekeeper for all geospatial data acquisition, ensuring compliance with best practices and drive system development and process set-up to meet “Data Factory” requirements.
• Data Onboarding & Integration: Oversee the ingestion and integration of third-party data into MSCI systems. Collaborate with product and research teams to prioritize data acquisition efforts.
• Data Governance Best Practices: Establish and enforce data governance standards for SLA management, new data acquisitions, source changes, and data cataloguing.
• Process Transformation: Optimize and scale existing data operations by implementing next-generation data acquisition workflows, leveraging Artificial Intelligence (AI) and cloud-native solutions for improved efficiency and quality.
• Data Solutions for Research & Product Teams: Partner with research and product teams to develop new methodologies, assess feasibility, and provide practical recommendations on data-driven initiatives.
• Operations Process Automation: Drive automation of manual processes through hands-on tactical solutions while implementing scalable, long-term production systems.
• Data QA Engineering: Develop and enhance quality assurance (QA) frameworks for new and existing data. Build efficient QA pipelines, multi-vendor validation systems, and data certification processes. Document findings and share insights with relevant research and product committees

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  Drive Data Collection & Delivery: Source, analyse, and process company disclosures, geospatial datasets, and public filings to produce accurate, timely Climate and ESG data. Own end-to-end collection cycles, vendor coordination, and SLA tracking.
• Leverage AI for Operational Excellence: Use generative AI and LLM tools in daily workflows for data extraction, content classification, anomaly detection, and research acceleration. Serve as the domain-expert validation layer for AI-generated outputs.
• Develop AI-Friendly Methodologies: Design and document collection approaches optimised for AI-assisted execution—supporting the systematic conversion of unstructured disclosures into structured, machine-readable data at scale.
• Strengthen Quality Assurance: Collaborate with QA engineers to design contextual QA checks, multi-layer validation frameworks, and vendor comparison systems. Use data patterns to refine detection logic and reduce manual intervention.
• Enable New Methodologies & Products: Partner with Research and Product teams to assess data feasibility, execute pilot collections for new coverage areas (e.g., physical risk, supply-chain data), and contribute operational insights to product design.
• Maintain & Evolve the Ops Platform: Own operational trackers, dashboards, and reference systems. Identify gaps, propose improvements, and implement enhancements to keep the platform current and scalable.
• Operational Reporting & Documentation: Develop SOPs, process flows, and knowledge-base articles that ensure transparency, audit-readiness, and scalable knowledge transfer.
• Collaborate & Communicate: Work with Research, Technology, and downstream teams to align on data requirements and ensure seamless delivery. Address client and issuer queries with clear, professional communication.

• 1–3 years of experience in data collection, research, or data management; ESG/Climate experience is a strong advantage.
• Strong understanding of, or interest in, Climate and ESG data domains
• Hands-on Python programming (minimum 1 year); ability to script, analyse data, and automate operational tasks.
• Active user of generative AI / LLM tools (e.g., ChatGPT, Claude, Copilot) for research and data analysis; demonstrated drive to adopt GenAI into production workflows.
• Working knowledge of SQL; familiarity with geospatial tools (QGIS, Google Earth Engine) is a plus.
• Strong attention to detail, structured analytical mindset, and excellent English communication skills (written and verbal).
• Comfortable managing competing priorities in a deadline-driven, cross-functional, global team environment.
• Proactive, curious, and willing to challenge the status quo to improve operational processes.

• Transparent compensation schemes and comprehensive employee benefits, tailored to your location, ensuring your financial security, health, and overall wellbeing.
• Flexible working arrangements, advanced technology, and collaborative workspaces.
• A culture of high performance and innovation where we experiment with new ideas and take responsibility for achieving results.
• A global network of talented colleagues, who inspire, support, and share their expertise to innovate and deliver for our clients.
• Global Orientation program to kickstart your journey, followed by access to our Learning@MSCI platform, AI Learning Center , LinkedIn Learning Pro and tailored learning opportunities for ongoing skills development.
• Multi-directional career paths that offer professional growth and development through new challenges, internal mobility and expanded roles.
• We actively nurture an environment that builds a sense of inclusion belonging and connection, including eight Employee Resource Groups. All Abilities, Asian Support Network, Black Leadership Network, Climate Action Network, Hola! MSCI, Pride & Allies, Women in Tech, and Women’s Leadership Forum.

MSCI strengthens global markets by connecting participants across the financial ecosystem with a common language. Our research-based data, analytics and indexes, supported by advanced technology, set standards for global investors and help our clients understand risks and opportunities so they can make better decisions and unlock innovation. We serve asset managers and owners, private-market sponsors and investors, hedge funds, wealth managers, banks, insurers and corporates.