Testing Manager - Analytics and AI Evaluation 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.
• Bachelor's 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.