At a Glance
- Tasks: Lead innovative AI and ML projects that create real value for clients.
- Company: Join Moody's, a global leader in risk assessment and AI transformation.
- Benefits: Inclusive culture, competitive salary, and opportunities for professional growth.
- Other info: Collaborate with a diverse team and drive responsible AI practices.
- Why this job: Be at the forefront of AI, shaping impactful solutions for real-world challenges.
- Qualifications: Experience in data science, machine learning, and strong Python programming skills.
The predicted salary is between 63000 - 77000 £ per year.
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.
- 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.
- Strong working knowledge of large language models (LLMs), including prompting, fine-tuning, retrieval-augmented generation (RAG), and evaluation techniques.
- Experience with agentic AI frameworks and libraries in Python, such as AWS Bedrock AgentCore, LangChain/LangGraph, CrewAI, or the OpenAI Agents SDK.
- 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.
- 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.
Let's begin! Assistant Director -Data Science in London employer: Moody's
Moody's is an exceptional employer that fosters an inclusive and innovative work culture, where every employee is encouraged to express their ideas and contribute to meaningful projects. With a strong focus on professional growth, employees have access to diverse training opportunities and the chance to work alongside industry leaders in catastrophe risk management. Located in a vibrant environment, Moody's not only prioritises employee well-being but also champions diversity, making it a rewarding place for those looking to make a significant impact in their careers.
StudySmarter Expert Advice🤫
We think this is how you could land Let's begin! Assistant Director -Data Science in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Let's begin! Assistant Director -Data Science in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Moody's, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Moody's. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Moody's
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Moody's!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
