Assc Dir Mgr-Data Management

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

• 6+ years of experience in data operations, data quality, remediation, or scaled service delivery.
• Demonstrated success managing large operational teams (30+ resources).
• Strong understanding of QA methodologies, defect management, and quality controls.
• Hands on proficiency with Excel and SQL.
• Proven ability to run process driven, high throughput operations with strong governance.
• Experience using Python and Databricks for scalable data analysis and remediation.
• Exposure to AI/ML enabled remediation or automation testing.
• Familiarity with data governance concepts such as lineage, controls, and auditability.
• Experience operating in regulated or high scrutiny data environments.

Education 

• Bachelor’s degree in Data, Business, Engineering, or related field (or equivalent experience).

Responsibilities

• Scaled Remediation Delivery (All Clients)
  • Own end to end execution of scaled remediation across all client segments, ensuring throughput, consistency, and auditability.
  • Operate a standardized remediation lifecycle: intake → triage → pattern identification → remediation execution → QA validation → release.
  • Ensure remediation output meets data governance standards, quality thresholds, and product expectations.
  • Balance speed and quality to maintain predictable delivery at high volume.
• Pattern Detection & Mass Remediation
  • Lead pattern detection efforts to identify repeatable data defects suitable for mass remediation.
  • Partner with technical team members to convert patterns into programmatic remediation logic using SQL, Python, and Databricks.
  • Maintain a prioritized remediation pattern backlog, aligned to volume reduction and quality impact.
  • Drive proactive remediation by analyzing trends, recurrence, and defect clustering across datasets.
• AI Remediation Pattern Testing & Quality Control
  • Lead a dedicated team responsible for testing and validating AI assisted remediation patterns before scaled deployment.
  • Define QA frameworks for AI remediation, including:
    • Golden datasets and expected outcomes
    • Precision/recall and consistency testing
    • Regression and drift monitoring
    • Guardrails, thresholds, and rollback criteria
  • Ensure AI remediation outputs are safe, consistent, explainable, and aligned with governance standards.
• Quality Assurance & Validation
  • Oversee QA testers responsible for sampling, regression testing, and defect validation across manual and automated remediation.
  • Define quality gates, acceptance criteria, and rework thresholds.
  • Track quality metrics (defect leakage, rework rates, false positives/negatives) and drive continuous improvement.
  • Ensure documentation and evidence are maintained to support audit and review needs.
• Team Leadership & Workforce Management (30+ Resources)
  • Lead and develop a large, multi layered organization including:
    • Manual remediation specialists
    • QA testers
    • AI remediation pattern test teams
    • Technical remediation resources\
    • Contractors flexed across workflow stages
  • Set clear expectations, performance metrics, and operating rhythms across teams.
  • Manage staffing models and capacity planning to support volume spikes and backlog reduction.
  • Coach frontline managers and senior individual contributors to scale leadership effectiveness.
• Contractor & Vendor Management
  • Manage day to day contractor engagement, including onboarding, training, workload allocation, and performance oversight.
  • Ensure contractors adhere to remediation playbooks, quality standards, and access controls.
  • Adjust contractor mix and coverage based on demand, automation progress, and remediation priorities.
• Metrics, Reporting & Operational Discipline
  • Own operational KPIs including throughput, cycle time, backlog size, quality rates, automation coverage, and SLA attainment.
  • Provide structured, executive ready reporting to senior stakeholders.
  • Identify operational bottlenecks and drive corrective actions through process, tooling, or staffing changes.
  • Establish predictable delivery through disciplined planning, forecasting, and execution tracking.
• Cross Functional Partnership
  • Partner with Product, Data Governance, Engineering, and upstream data teams to align on remediation standards and priorities.\
  • Escalate systemic issues and contribute to long term prevention strategies.
  • Support broader remediation strategy by feeding insights on patterns, tooling gaps, and automation opportunities.
• Scope & Operating Model
  • Team Size: 30+ resources across multiple functions and workflows
  • Remediation Type: Scaled, pattern based, and programmatic
  • Core Focus: Volume reduction, quality consistency, automation enablement, and operational predictability
• Success Profile
  • Remediation backlogs are reduced predictably through pattern based and automated approaches.
  • Quality improves with strong QA gating and low rework rates.
  • AI remediation patterns are validated, governed, and safely deployed at scale.
  • Manual effort declines over time as automation coverage increases.
  • Stakeholders have confidence in metrics, forecasts, and delivery commitments.

About the team

Our Data Estate team is responsible for managing, enriching, and validating the entity data that powers Moody’s financial intelligence products. We contribute to Moody's by providing the trusted foundational data our clients rely on for risk assessment and decision-making.
By joining our team, you will be part of the critical work of maintaining a world-class data platform that helps businesses navigate the global marketplace and abundant opportunities for learning, professional growth, and career advancement - all while working alongside passionate colleagues in a dynamic environment.

Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.

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.