Let's begin! Data Analyst (13211)

Let's begin! Data Analyst (13211)
Requisition ID 13211 - Posted - Remote Worker - GER40

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.
If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.
Skills and Competencies

Experience running or supporting data labeling and annotation efforts, including campaign management, QA, or vendor coordination
Hands-on familiarity with professional labeling platforms such as Labelbox, Dataloop, Scale, or similar tools
Strong analytical mindset with the ability to interpret label distributions, consensus metrics, and error patterns
Working knowledge of Python for analysis and automation, including use of Jupyter notebooks
Comfort working with spreadsheets and basic analysis using SQL and/or Python
Strong operational and project management skills, with the ability to manage multiple concurrent campaigns
High attention to detail and rigor in documentation, taxonomy definition, and QA processes
Clear written and verbal communication skills for collaboration with technical teams and external vendors
Ability to give and receive feedback constructively to improve labeling instructions and outcomes
Exposure to computer vision, geospatial data, or ML workflows is an advantage

Education
Master's degree in a quantitative, analytical, or technical field
Responsibilities
This role owns the end-to-end lifecycle of ground truth data collection campaigns, translating machine learning needs into high-quality labeled datasets that power core computer vision products.

Partner with ML and Data Science leads to translate model requirements into clear label taxonomies and concrete labeling tasks
Design and run internal gold-standard campaigns to validate taxonomies and ensure coverage of edge cases
Configure and manage annotation projects, including uploading imagery, geometries, and metadata, and exporting labeled data
Coordinate with external labeling vendors, including taxonomy training, example walkthroughs, and ongoing feedback
Evaluate vendor performance using gold datasets, confusion matrices, and quality reports to determine readiness for scale
Launch and monitor production labeling campaigns, tracking throughput, SLAs, and overall progress
Triage worker questions, resolve taxonomy ambiguities, and proactively identify tooling or workflow issues
Monitor label quality and consensus metrics, initiating additional vote rounds or manual QA where needed
Review low-agreement areas of interest and propose taxonomy refinements, clarifications, or deprecations
Build final ground truth datasets, ensuring quality thresholds are met and data is clearly documented for ML consumption
Maintain campaign status and documentation so stakeholders have clear visibility into data readiness
Contribute to continuous improvement of ground truth processes, tooling, and best practices

About the Team
You will join Cape by Moody’s, a team focused on delivering highly precise property insights derived from aerial imagery and advanced machine learning. Ground truth data is central to the team’s mission, and this role works closely with data scientists, ML engineers, and product partners across Europe and North America in a collaborative, remote-friendly environment.
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.

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At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.
If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.
Skills and Competencies

Experience running or supporting data labeling and annotation efforts, including campaign management, QA, or vendor coordination
Hands-on familiarity with professional labeling platforms such as Labelbox, Dataloop, Scale, or similar tools
Strong analytical mindset with the ability to interpret label distributions, consensus metrics, and error patterns
Working knowledge of Python for analysis and automation, including use of Jupyter notebooks
Comfort working with spreadsheets and basic analysis using SQL and/or Python
Strong operational and project management skills, with the ability to manage multiple concurrent campaigns
High attention to detail and rigor in documentation, taxonomy definition, and QA processes
Clear written and verbal communication skills for collaboration with technical teams and external vendors
Ability to give and receive feedback constructively to improve labeling instructions and outcomes
Exposure to computer vision, geospatial data, or ML workflows is an advantage

Education
Master's degree in a quantitative, analytical, or technical field
Responsibilities
This role owns the end-to-end lifecycle of ground truth data collection campaigns, translating machine learning needs into high-quality labeled datasets that power core computer vision products.

Partner with ML and Data Science leads to translate model requirements into clear label taxonomies and concrete labeling tasks
Design and run internal gold-standard campaigns to validate taxonomies and ensure coverage of edge cases
Configure and manage annotation projects, including uploading imagery, geometries, and metadata, and exporting labeled data
Coordinate with external labeling vendors, including taxonomy training, example walkthroughs, and ongoing feedback
Evaluate vendor performance using gold datasets, confusion matrices, and quality reports to determine readiness for scale
Launch and monitor production labeling campaigns, tracking throughput, SLAs, and overall progress
Triage worker questions, resolve taxonomy ambiguities, and proactively identify tooling or workflow issues
Monitor label quality and consensus metrics, initiating additional vote rounds or manual QA where needed
Review low-agreement areas of interest and propose taxonomy refinements, clarifications, or deprecations
Build final ground truth datasets, ensuring quality thresholds are met and data is clearly documented for ML consumption
Maintain campaign status and documentation so stakeholders have clear visibility into data readiness
Contribute to continuous improvement of ground truth processes, tooling, and best practices

About the Team
You will join Cape by Moody’s, a team focused on delivering highly precise property insights derived from aerial imagery and advanced machine learning. Ground truth data is central to the team’s mission, and this role works closely with data scientists, ML engineers, and product partners across Europe and North America in a collaborative, remote-friendly environment.
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.


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