Data Value Analyst

Skills and Competencies

• 7+ years of relevant professional experience in valuation, financial analysis, data products, licensing, royalties, pricing, commercial strategy, or a related discipline
• This role requires a commercially minded valuation professional who can combine sound analysis, practical judgment, and cross-functional influence to determine and defend the relative value of data within blended products.
• Data valuation expertise and judgment, including the ability to estimate how individual datasets contribute to the value of blended products and recognize that value varies by customer, use case, product design, substitutes, exclusivity, timeliness, quality, coverage, and strategic importance
• Strong knowledge of valuation approaches, including comparable-product, market, income, cost, contribution, willingness-to-pay, scenario, and sensitivity analyses, with the judgment to establish defensible ranges without implying false precision
• Advanced analytical, financial modeling, and tool proficiency, with the ability to gather, validate, and interpret product performance, pricing, margin, usage, retention, customer behavior, market, and competitive data using advanced spreadsheets and, where appropriate, SQL, Python, R, business intelligence platforms, or similar technologies
• Market and customer research experience, including surveys, interviews, benchmark studies, analysis of customer choices and willingness to pay, and interpretation of commercial evidence to inform valuation decisions
• Commercial, strategic, and stakeholder judgment, with the ability to connect valuation findings to product economics, customer outcomes, competitive differentiation, and go-to-market priorities while building alignment and managing trade-offs across Product, Data Operations, Royalties Management, Data Strategy, Finance, Sales or Go-to-Market, Risk, Procurement, Legal, and related functions
• Professional integrity, independence, and communication skills, with the ability to make and defend evidence-based decisions that protect the enterprise and clearly explain methods, assumptions, uncertainty, and recommendations to technical and non-technical audiences
• Documentation, governance discipline, execution, and learning agility, producing transparent analysis and decision records that support legal, risk, audit, contractual, or regulatory review while managing several complex valuations, prioritizing by business impact and risk, and quickly understanding unfamiliar datasets, products, industries, and emerging use cases

Education

• Bachelor’s or master’s degree, or equivalent experience, in finance, business administration, economics, or a related field
• Experience in data licensing, royalties, transfer pricing, intangible-asset valuation, B2B information or subscription products, or regulated data environments; a relevant credential, such as the CFA, ASA, or CPA, is advantageous but not required

Responsibilities

Lead the analysis needed to determine, document, communicate, implement, and maintain the relative value of data from multiple sources within blended products.

• Define the valuation scope by clarifying the product’s intended use cases, target customers, commercial objectives, pricing and packaging, contractual context, and the decision the analysis must support
• Build a clear fact base by identifying the source, content, coverage, quality, timeliness, usage rights, transformation, and role of each dataset in the final product
• Assess each dataset’s contribution to product functionality, customer outcomes, differentiation, revenue potential, cost, and go-to-market value
• Apply appropriate valuation methods based on the available evidence, including relevant precedents, peer products, market benchmarks, cost and income information, willingness-to-pay findings, and scenario analysis
• Evaluate internal and external comparisons and adjust for meaningful differences in product design, data composition, customer segment, use case, geography, contract terms, and market conditions
• Analyze market and customer evidence, including pricing trends, product performance, usage, retention, win/loss results, customer behavior, research findings, and competitive offerings
• Identify substitute data and alternative solutions and develop reasonable assumptions about their value, availability, switching cost, quality, coverage, and suitability for the intended use case
• Develop a defensible recommendation by reconciling incomplete or conflicting evidence, testing key assumptions, and identifying uncertainty, limitations, and factors that could change the conclusion
• Prepare valuation memoranda that clearly explain the evidence, methods, assumptions, calculations, and rationale supporting low, high, and recommended value or royalty-allocation ranges
• Maintain complete decision records with source evidence, model versions, stakeholder input, approvals, effective dates, review triggers, and exceptions sufficient for governance, audit, contractual, or regulatory review
• Present and defend recommendations to relevant stakeholders, explain trade-offs clearly, respond to challenges, and preserve the independence and integrity of the analysis
• Implement approved determinations by providing complete and accurate allocation data to downstream royalty, finance, product, contract, reporting, and related systems or processes, and by confirming that the determination was applied correctly
• Review valuations periodically and when material changes occur in data sources, product design, use cases, pricing, customer behavior, market conditions, contracts, regulation, or go-to-market strategy
• Provide subject-matter support to Legal and Risk on data value, licensing and royalty terms, valuation assumptions, controls, disputes, due diligence, and contractual or regulatory changes
• Improve the valuation framework by standardizing methods, templates, evidence requirements, review cycles, and quality controls, and by maintaining a useful library of precedents and comparable products

About the Team

The Moody’s Analytics Data Estate Data Governance team helps ensure that data is trusted, consistent, well understood, and used responsibly as a business asset. The team sets governance policies, standards, decision rights, and accountability; supports data ownership and stewardship; develops shared data models; and strengthens data quality, metadata, lineage, reference and master data, and lifecycle practices. Working across business, product, data, technology, Legal, and Risk teams, it turns enterprise data strategy into practical ways of working that make data easier to find, understand, combine, protect, and use for products, analytics, artificial intelligence, regulatory compliance, and business decisions.