Testing Manager – Analytics & AI Evaluation Center of Excellence Lead

You will oversee the end-to-end validation lifecycle for AI-powered products predictive analytics and HR technology solutions while driving the transformation of evaluation processes through automation. This role requires a strong systems-thinking mindset exceptional stakeholder management capabilities and the ability to translate complex technical concepts into actionable business insights.

Job Responsibilities

• Lead the end-to-end User Acceptance Testing (UAT) business validation and evaluation processes for HR analytics products and enterprise AI solutions.
• Define and execute validation strategies that ensure AI models predictive analytics frameworks and data-driven solutions meet business regulatory and operational requirements.
• Drive the modernization and automation of validation capabilities by implementing scalable testing frameworks automated regression suites and AI evaluation methodologies.
• Establish robust approaches for evaluating Large Language Models (LLMs) and AI-driven solutions including assessments of model quality business relevance fairness bias mitigation and data integrity.
• Partner closely with HR leaders product managers data scientists and technology teams to ensure seamless alignment between business requirements and technical solutions.
• Translate complex model performance metrics and validation outcomes into clear risk-based business recommendations for executive stakeholders.
• Build develop and lead a high-performing team focused on validation excellence innovation automation and continuous improvement.
• Promote a culture of operational rigor technical curiosity accountability and collaboration across the organization.
• Ensure adherence to data privacy governance compliance and ethical AI standards when evaluating HR-related solutions and sensitive workforce data.
• Identify opportunities to improve efficiency scalability and effectiveness of validation processes through innovative technologies and automation.

Required Qualifications Capabilities and Skills

• Proven experience leading validation testing analytics risk operations or related functions within a complex matrixed organization.
• Strong understanding of enterprise AI capabilities Large Language Models (LLMs) machine learning concepts and emerging AI technologies.
• Experience establishing leading or overseeing automated testing frameworks validation programs or business evaluation processes.
• Demonstrated knowledge of software development lifecycles data analytics ecosystems and enterprise technology delivery methodologies.
• Strong understanding of structured and unstructured data processing analytics pipelines and data quality principles.
• Experience evaluating business outcomes model performance and operational effectiveness through data-driven methodologies.
• Exceptional stakeholder management and influencing skills with the ability to engage effectively across technical and non-technical audiences.
• Demonstrated success leading teams managing talent and scaling operational capabilities through automation and process transformation.
• Strong verbal and written communication skills with the ability to present complex information to executive stakeholders.
• Experience managing data privacy compliance governance and ethical considerations associated with sensitive workforce or enterprise data.

Preferred Qualifications Capabilities and Skills

• Advanced business degree (MBA or equivalent) or graduate-level qualification in analytics data science artificial intelligence or a related discipline.
• Bachelors degree in Engineering Computer Science Information Technology Analytics or a related technical field.
• Experience within financial services banking or other highly regulated industries.
• Familiarity with AI evaluation frameworks prompt testing methodologies synthetic data generation techniques and model benchmarking approaches.
• Working knowledge of Python SQL automation testing tools or technologies used to validate analytics platforms data pipelines and AI models.
• Experience driving enterprise-scale transformation initiatives focused on automation digital modernization or AI adoption.