Let's begin! 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

• Bachelors or masters 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 products 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 datasets 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 Moodys 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.