Senior Data Analytics Leader: AI-Enabled Insights

Moody's

New York (NY)

On-site

USD 143,000 - 207,000

Full time

14 days+

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Benefits offered by this job

Medical

Dental

Vision

Parental leave

Paid time off

401(k) plan with employer contribution

Life insurance

Disability insurance

Accident insurance

Discounted employee stock purchase计划

Job summary

Moody's Investors Service seeks a senior analytics leader to design and deliver trusted analytics, reporting, data products, and AI-enabled solutions across finance and risk platforms. You will partner with BI, FP&A, Sales Analytics, and other stakeholders to elicit requirements, translate problems into user stories, and guide data modeling, semantic layers, and AI-driven insights.

This role emphasizes data quality, governance, and scalable analytics in a fast-paced environment, with

Qualifications

10+ years of business analysis, requirements gathering, and stakeholder management in BI, analytics, or data product delivery.
Ability to translate ambiguous business problems into structured requirements, user stories, functional specs, and testable acceptance criteria.
Strong partnership skills across business and technology teams to influence design and deliver practical solutions.
Experience designing data products, semantic models, reporting frameworks, and reusable analytics with engineering teams.

Responsibilities

Partner with business and technology teams to design and deliver trusted analytics, reporting, data products, and AI-enabled solutions.
Elicit, clarify, and prioritize reporting, analytics, and data product requirements with BI, FP&A, Sales Analytics, and others.
Translate business problems into clear user stories, requirements, data definitions, and acceptance criteria.
Evaluate and recommend approaches across Databricks, reporting layers, analytics workflows, and AI-enabled solutions.
Lead workshops with stakeholders, manage expectations and scope.
Support data modeling aligned to business semantics including dimensions, measures, hierarchies, grain, and lineage.
Contribute to reporting design, mapping, semantic layers, and data consumption patterns.
Test reports and datasets end-to-end, validate against source systems and business rules.
Ensure data quality, governance, usability and identify AI-driven insights opportunities.