Data Scientist, Strategic Intelligence

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 rolesStrong Python skills (e.g., pandas, scikit-learn, statsmodels, NLP and deep learning libraries) and proficient SQLHands-on experience building predictive models (classification, regression, survival, time-series), including feature engineering, validation, and monitoringExperience 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 experimentationExperience 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 modelsFamiliarity with BI platforms such as Power BI or Tableau for delivering model outputsAbility to translate business problems into analytical approaches and quantify commercial impactExperience working with commercial, customer, financial, risk, product, or operational dataStrong problem-solving and critical thinking skills, with sound judgement on model limitations and uncertaintyAbility to communicate effectively with both technical and non-technical audiencesPreferred: Background in economics or econometrics, with a strong grounding in modern economic thinkingPreferred: Experience in credit risk, financial services, risk analytics, SaaS, or enterprise software organizationsPreferred: Experience with customer analytics such as propensity, churn, account health, or lifetime value modellingPreferred: Experience with external data sources (e.g., news, filings, firmographic or alternative data) and entity resolutionPreferred: Experience with Unity Catalog, Delta Lake, Azure ML, or Azure OpenAIPreferred: Experience building and deploying AI-enabled applications, including web-hosted BI productsDeep 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 fieldA Master's degree or PhD is preferredEquivalent practical experience will also be consideredRelevant 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 modelsConvert unstructured content into structured, governed signals and features that can be used in analytics and modelsCombine internal data with external data and information sources to enrich account, customer, and market viewsEvaluate and onboard new external data sources, assessing quality, coverage, licensing, and fit for purposeDevelop, validate, and maintain propensity-to-buy models that help commercial teams prioritize customers and prospectsBuild account health scoring and churn prediction models that identify retention risk earlyQuantify customer and portfolio risk using statistical and machine learning techniques, flagging emerging issues to the businessIdentify and size growth opportunities (e.g., cross-sell, upsell, whitespace) with transparent assumptions and sensitivity analysisApply machine learning and generative AI to improve forecasting, segmentation, and decision intelligenceDesign AI-enabled workflows (e.g., summarization, entity extraction, signal detection) with appropriate human review and controlsPrototype rapidly, test with experiments, and measure the business impact of models and AI solutionsTrack emerging AI and machine learning techniques and recommend where they add value for the teamOperationalize models in Databricks and Azure with versioning, testing, and monitoring for performance and driftPartner with analytical engineers and Commercial Data Engineering to build feature pipelines and curated datasetsDeliver model outputs through dashboards, Power BI, Tableau, and web-hosted BI applicationsDocument data sources, features, assumptions, and model lineagePartner with Commercial Strategy, Customer Success, Sales, Finance, Product, and Technology teams to frame business questions as analytical problemsTranslate model results into clear, actionable recommendations for technical and non-technical audiencesDefine success metrics with stakeholders and track the value delivered by analytical solutionsSupport prioritization of analytical investments based on business value and strategic objectivesApply sound model governance, including validation, back-testing, explainability, and bias checksEnsure data used in models is accurate, complete, and fit for purpose, working with domain owners to resolve quality issuesUse data and AI tools only within approved Moody's environments, in line with data classification and information security policiesContribute 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.