Assistant Director -Data Science (Machine Learning Engineering)

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 and/or exposure in data science, machine learning, or applied artificial intelligence, including experience working on projects or collaborating with data scientists and engineers
• Hands-on experience building, training, and evaluating machine learning and deep learning models, including modern architectures such as transformers, with the ability to assess where advanced approaches outperform classical methods and where they do not
• Strong programming skills in Python, with practical experience deploying machine learning models and services into production environments
• Practical experience using AI coding assistants and agentic developer tools such as Claude Code and OpenAI Codex to accelerate development, testing, and code review, with familiarity across the software development lifecycle and machine learning operations practices
• Strong working knowledge of large language models (LLMs), including prompting, fine-tuning, retrieval-augmented generation (RAG), and evaluation techniques, and the ability to apply them to real product challenges
• Experience with agentic AI frameworks and libraries in Python, such as AWS Bedrock AgentCore, LangChain/LangGraph, CrewAI, or the OpenAI Agents SDK, with an understanding of agent design patterns including tool use, orchestration, memory, and multi-agent workflows
• Ability to own the full model-development lifecycle, including problem framing, data exploration, solution design, estimation, validation, deployment, and ongoing monitoring
• Ability to explain, present, and demonstrate complex modeling work clearly to senior leaders, cross-functional partners, and non-technical stakeholders
• Working knowledge of cloud-based data and machine learning platforms such as AWS, Azure, GCP, and Databricks, as well as the broader ecosystem used to build and deploy AI and ML products
• Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency. Strong experience using AI tools to lead innovation initiatives. Demonstrated leadership in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization.

Education

• Degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field is required; a Master's or Ph.D. in a related discipline is preferred, but not required

Responsibilities

Lead the design, development, and delivery of innovative AI, ML, and GenAI solutions that create meaningful value for clients and advance product capabilities across the Corporate and Governments business.

• Lead the end-to-end design, development, and deployment of machine learning, AI, and GenAI solutions that enhance existing products and enable new client-facing capabilities
• Partner with product, engineering, and business stakeholders across the Corporate and Governments business to translate client needs and market opportunities into well-scoped data science initiatives
• Own model selection, experimentation, and validation end-to-end, ensuring solutions are accurate, scalable, compliant with applicable regulations and legal frameworks, and production-ready
• Identify opportunities for automation and model-based enhancement, applying machine learning and deep learning methods to improve accuracy, efficiency, and overall performance
• Foster best practices in coding, experimentation, machine learning operations, and software engineering excellence
• Drive responsible AI practices across the team, including risk management, model governance, and the ethical use of AI and machine learning techniques
• Communicate technical work clearly and concisely, ensuring insights, limitations, and implications are understood by a broad range of stakeholders, including senior leaders

About the Team

Our Data Science, Machine Learning & Engineering (DSMLE) team, part of the broader Corporate and Governments (C&G) Data Team, is responsible for designing, building, and enhancing products through AI, machine learning, and generative AI solutions that deliver innovation and value to clients. By joining the team, you will work at the forefront of applied AI and GenAI, translating advanced techniques into client-facing products, collaborating with a global cross-functional data organization, and helping shape a growing practice focused on the responsible, ethical, and impactful use of artificial intelligence.

#LI-Hybrid

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.