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
- 5+ years of experience in data science, applied machine learning, or quantitative modelling roles
- Strong Python skills (e.g., pandas, scikit-learn, statsmodels, NLP and deep learning libraries) and proficient SQL
- Hands-on experience building predictive models (classification, regression, survival, time-series), including feature engineering, validation, and monitoring
- Experience with NLP and unstructured text data, and familiarity with LLM and generative AI techniques (e.g., embeddings, retrieval, prompt design, evaluation)
- Solid foundation in statistics, econometrics, and causal inference or experimentation
- Experience with Databricks, Spark, MLflow, or comparable cloud-based data and ML platforms (Azure preferred)
- Experience working with source control, testing, and deployment best practices for models
- Familiarity with BI platforms such as Power BI or Tableau for delivering model outputs
- Ability to translate business problems into analytical approaches and quantify commercial impact
- Experience working with commercial, customer, financial, risk, product, or operational data
- Strong problem-solving and critical thinking skills, with sound judgement on model limitations and uncertainty
- Ability to communicate effectively with both technical and non-technical audiences
- Preferred: Background in economics or econometrics, with a strong grounding in modern economic thinking
- Preferred: Experience in credit risk, financial services, risk analytics, SaaS, or enterprise software organizations
- Preferred: Experience with customer analytics such as propensity, churn, account health, or lifetime value modelling
- Preferred: Experience with external data sources (e.g., news, filings, firmographic or alternative data) and entity resolution
- Preferred: Experience with Unity Catalog, Delta Lake, Azure ML, or Azure OpenAI
- Preferred: Experience building and deploying AI-enabled applications, including web-hosted BI 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
- Bachelor's degree in Economics, Statistics, Mathematics, Data Science, Computer Science, Engineering, or a related quantitative field
- A Master's degree or PhD is preferred
- Equivalent practical experience will also be considered
- Relevant certifications in Databricks, cloud platforms, or machine learning are preferred but not required
Responsibilities
The Data Scientist builds AI-enabled predictive models and analytics capabilities that turn internal and external data into trusted, explainable intelligence across commercial, customer, product, and portfolio use cases.
- Ingest and interpret qualitative and unstructured information (e.g., news, filings, CRM notes, customer interactions, survey and support text) using NLP and large language models
- Convert unstructured content into structured, governed signals and features that can be used in analytics and models
- Combine internal data with external data and information sources to enrich account, customer, and market views
- Evaluate and onboard new external data sources, assessing quality, coverage, licensing, and fit for purpose
- Develop, validate, and maintain propensity-to-buy models that help commercial teams prioritize customers and prospects
- Build account health scoring and churn prediction models that identify retention risk early
- Quantify customer and portfolio risk using statistical and machine learning techniques, flagging emerging issues to the business
- Identify and size growth opportunities (e.g., cross-sell, upsell, whitespace) with transparent assumptions and sensitivity analysis
- Apply machine learning and generative AI to improve forecasting, segmentation, and decision intelligence
- Design AI-enabled workflows (e.g., summarization, entity extraction, signal detection) with appropriate human review and controls
- Prototype rapidly, test with experiments, and measure the business impact of models and AI solutions
- Track emerging AI and machine learning techniques and recommend where they add value for the team
- Operationalize models in Databricks and Azure with versioning, testing, and monitoring for performance and drift
- Partner with analytical engineers and Commercial Data Engineering to build feature pipelines and curated datasets
- Deliver model outputs through dashboards, Power BI, Tableau, and web-hosted BI applications
- Document data sources, features, assumptions, and model lineage
- Partner with Commercial Strategy, Customer Success, Sales, Finance, Product, and Technology teams to frame business questions as analytical problems
- Translate model results into clear, actionable recommendations for technical and non-technical audiences
- Define success metrics with stakeholders and track the value delivered by analytical solutions
- Support prioritization of analytical investments based on business value and strategic objectives
- Apply sound model governance, including validation, back-testing, explainability, and bias checks
- Ensure data used in models is accurate, complete, and fit for purpose, working with domain owners to resolve quality issues
- Use data and AI tools only within approved Moody's environments, in line with data classification and information security policies
- Contribute to data science standards and best practices across the Business Intelligence team
About the Team
The Strategic and Operational Analytics team develops trusted data products, analytics, and decision-support capabilities that serve Moody's Analytics leadership and business teams. The team works across commercial data foundations, customer intelligence, product analytics, pricing, portfolio management, market analytics, and AI-enabled decision intelligence. This role plays a critical part in expanding the team's predictive analytics and AI capabilities, turning internal and external data into account and product intelligence that powers decision making across the Commercial Analytics portfolio. The position partners closely with Commercial Data Engineering, Product Strategy, Commercial Strategy, Customer Success, Finance, and Technology Services Group teams to create scalable, governed, and business-aligned data science products.
For US-based roles only: the anticipated hiring base salary range for this position is $116,500.00 - $169,000.00, depending on factors such as experience, education, level, skills, and location. This range is based on a full-time position. In addition to base salary, this role is eligible for incentive compensation. Moody’s also offers a competitive benefits package, including not but limited to medical, dental, vision, parental leave, paid time off, a 401(k) plan with employee and company contribution opportunities, life, disability, and accident insurance, a discounted employee stock purchase plan, and tuition reimbursement.
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, gender, age, religion or creed, national origin, ancestry, citizenship, marital or familial status, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, military or veteran status, or any other characteristic protected by law. Moody’s also provides reasonable accommodation to qualified individuals with disabilities or based on a sincerely held religious belief in accordance with applicable laws. If you need to inquire about a reasonable accommodation, or need assistance with completing the application process, please email accommodations@moodys.com. This contact information is for accommodation requests only, and cannot be used to inquire about the status of applications
For San Francisco positions, qualified applicants with criminal histories will be considered for employment consistent with the requirements of the San Francisco Fair Chance Ordinance.
This position may be considered a promotional opportunity, pursuant to the Colorado Equal Pay for Equal Work Act.
Click here to view our full EEO policy statement. Click here for more information on your EEO rights under the law. Click here to view our Pay Transparency Nondiscrimination statement.
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.
