Director of Standards and Integration

NO THIRD PARTIES PLEASE

Director of Standards and Integration (Right to Hire)

You will lead and coordinate assurance execution for enterprise data quality and Responsible AI controls, ensuring that prioritized data products, critical data elements, AI products, models, and GenAI solutions are assessed against defined standards, thresholds, and evidence expectations. The role requires strong technical judgment, disciplined execution, and the ability to work across global stakeholders to convert governance expectations into repeatable testing, monitoring, and reporting practices.

You will help build and scale an integrated Data Quality and AI assurance capability that supports intake, risk classification, pre-release review, production monitoring, exception management, and audit-ready documentation. This includes supporting the development of dashboards, KPI/KRI reporting, control execution routines, and evidence packages that provide transparent visibility into data quality health, AI risk posture, remediation progress, and adherence to enterprise policy and standards.

Responsibilities:

• Lead assurance execution for Data Quality and Responsible AI controls across prioritized data products, AI products, models, GenAI use cases, and enterprise control plane capabilities.

• Translate data and AI policy requirements into practical assurance procedures, test scripts, control checks, documentation standards, and evidence expectations.

• Define and execute data quality assessment routines, including profiling, completeness, accuracy, timeliness, consistency, validity, lineage, metadata quality, and fit-for-use reviews.

• Support Responsible AI assurance checkpoints across the AI lifecycle, including intake, risk tiering, pre-deployment review, production monitoring, periodic review, and change governance.

• Review AI and data product evidence for adherence to standards, including approved data source usage, documented controls, explainability artifacts, monitoring thresholds, exception handling, and remediation plans.

• Develop and maintain dashboards, scorecards, and KPI/KRI reporting that provide visibility into data quality health, AI risk indicators, control effectiveness, exceptions, and remediation progress.

• Coordinate issue triage, severity assessment, root cause documentation, remediation tracking, SLA monitoring, and closure evidence for Data Quality and Responsible AI findings.

• Partner with product owners, data domain teams, technology, MLOps, DataOps, model risk, legal, compliance, privacy, information security, and audit stakeholders to embed assurance practices into delivery workflows.

• Support pilots and scale-out of the integrated Data Quality and AI control plane, including executable rules, monitoring routines, exception management workflows, and reusable playbooks.

• Prepare governance materials, management reporting, and audit-ready documentation for working groups, leadership forums, control partners, and senior stakeholders.

• Identify opportunities to automate control testing, evidence capture, monitoring alerts, dashboarding, and policy-to-rule translation across enterprise platforms and tooling.

• Provide day-to-day leadership, coaching, and quality review for analysts, specialists, or execution partners supporting Data Quality and AI assurance activities in India and globally.

Skills and Expertise:

• Significant experience in data quality, data governance, AI governance, model risk, technology risk, audit, compliance, analytics, data engineering, or related disciplines, preferably in a large global or regulated organization.

• Strong understanding of data quality dimensions, profiling, rules, thresholds, controls, monitoring, issue management, remediation, lineage, metadata, and critical data element management.

• Working knowledge of Responsible AI concepts, including fairness, explainability, transparency, robustness, privacy, human oversight, model monitoring, drift, bias indicators, and AI lifecycle governance.

• Experience designing or executing assurance, control testing, evidence review, audit readiness, or quality review processes for data, analytics, AI, or technology products.

• Ability to translate policies, standards, and control requirements into executable procedures, testing routines, reporting requirements, and operational playbooks.

• Hands-on experience with reporting and dashboarding tools such as Power BI, Tableau, Grafana, Streamlit, Dash, or similar platforms.

• Familiarity with data governance, data quality, metadata, catalog, observability, ModelOps, or workflow tools such as Ataccama, Collibra, Informatica, Denodo, ServiceNow, Jira, or similar platforms.

• Strong analytical skills, with the ability to interpret data quality results, AI risk indicators, control failures, trends, exceptions, and remediation status.

• Excellent documentation and communication skills, with the ability to produce clear governance materials, executive-ready summaries, testing evidence, and issue narratives.

• Strong stakeholder management and collaboration skills across business, technology, risk, compliance, legal, audit, and global delivery teams.

• Demonstrated ability to lead execution in a matrixed environment, manage competing priorities, and drive outcomes with discipline, accountability, and attention to detail.

• Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, Statistics, Mathematics, Business Analytics, or a related field; advanced degree preferred.

Preferred Qualifications

• Experience in financial services, insurance, asset management, banking, or another highly regulated industry.

• Experience supporting global teams, including coordination across U.S. and India time zones.

• Familiarity with frameworks and standards such as NIST AI Risk Management Framework, ISO/IEC AI management standards, DAMA-DMBOK, DCAM, or similar data and AI governance frameworks.

• Exposure to AI/ML development, MLOps, GenAI, large language models, retrieval-augmented generation, model validation, or model monitoring practices.

• Experience with SQL, Python, R, data profiling, data observability, automated testing, or analytics engineering practices.

• Certifications such as CDMP, DGSP, CISA, CRISC, ISO, cloud data certifications, AI governance credentials, or relevant risk and compliance certifications.

What Success Looks Like

• Data Quality and AI assurance routines are executed consistently, with clear evidence, documented outcomes, and timely escalation of material risks or exceptions.

• Dashboards and reporting provide transparent visibility into data quality health, AI risk posture, control effectiveness, and remediation progress.

• Assurance findings are translated into practical remediation actions, tracked to closure, and used to improve standards, controls, and operating practices.

• Business, technology, and control partners understand what is required to demonstrate that data and AI products are compliant, monitored, and fit for purpose.

Send resumes to jvale@jsrtechconsulting.com