Associate Product Manager, Climate & Nature Solutions

The Team
Morningstar Sustainalytics' Climate and Nature Solutions team empowers investors, asset managers, and corporates with decision‑useful nature & climate related data products-spanning emissions metrics, decarbonization pathways, biodiversity risk, transition risk, physical risk, and climate scenario analysis. We combine trusted ESG research, advanced analytics, and responsible AI to help the market make climate-informed decisions.
The Role
We are seeking an Associate Product Manager with experience in climate and data‑driven work who is ready to grow into a product management role. This position is ideal for someone already comfortable working with datasets and technical teams, and motivated to build PM capabilities across discovery, road mapping, requirements, and stakeholder alignment.
You will partner closely with climate research, data operations, data science, and engineering to build high quality datasets and product capabilities for investors.
Responsibilities

• Work hands‑on with nature, climate and geospatial datasets (and the teams producing them) to help define data structures, quality checks, documentation/lineage, and evaluation metrics.
• Partner with data science and engineering to deliver AI‑enabled data pipelines and metrics, translating technical outputs into clear user value.
• Apply climate and location‑based context to product decisions, connecting investor use cases (e.g. engagement, reporting, product creation) to data and feature priorities.
• Drive execution by writing clear requirements, managing a prioritized backlog, and coordinating across research, platform, and go‑to‑market teams.
• Engage with clients and internal stakeholders to validate needs, test usability, and track adoption and impact.

Requirements

• 2-5 years of experience working with data centric products, including climate, sustainability or geospatial (GIS) datasets.
• Bachelor's degree in Economics, Finance, Data Science, Environmental Science, Computer Science, or a related field, or equivalent experience. Advanced degree in a related concentration is preferred.
• Comfort collaborating with data science and engineering teams, and enough technical fluency to discuss data pipelines, metrics, and quality trade‑offs.
• Familiarity with climate investing concepts such as financed emissions, target setting, transition and physical risk, climate scenarios, and portfolio alignment. Interest in professional development in climate or sustainable investing.
• A user‑focused mindset, with the ability to validate needs, incorporate feedback, and measure success through adoption and usage.
• Experience with ESG or climate data providers, geospatial or remote sensing data, or corporate emissions estimation. Understanding of climate disclosure frameworks such as ISSB and TCFD.
• Background in quantitative research, data engineering, or analytics supporting investment workflows.
• Exposure to cloud data platforms, feature stores, or ML operations. Familiarity with AI governance and model risk management best practices.

Example Projects You Might Work On

• Development of a company‑level climate dataset integrating reported and estimated emissions, target assessments, and transition plan indicators.
• Deployment of an AI‑assisted disclosure extraction pipeline to improve coverage and timeliness of climate‑related metrics.
• Deployment of geospatial related analytics to assess nature & climate related risk and opportunities.
• Enhancements to portfolio alignment metrics using scenario‑consistent trajectories and explainability features.

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.
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