Walter Yates

HR professional and Rutgers graduate with a background spanning HR consulting, AI evaluations, and talent acquisition. Experienced in delivering HR experience within talent acquisition, onboarding, benefits, payroll, and compliance across pharmaceutical, logistics, technology, and shipping industries. Brings a rare combination of expertise and technical credibility in AI model evaluation for HR, with additional strengths in competency modeling, employment law, employee relations, and bilingual (English) communication.

Dedicated to learning and pursuing skills as well as technology that will assist in pushing workforces towards the future.

Human Resources Consultant at Talent Growth HR (2023-09 – Present)

• Deliver end-to-end HR functions across recruiting, onboarding, offboarding, benefits, payroll, and employment law for clients in the pharmaceutical and shipping industries

• Built a competency model anchored in company culture and strategy for a pharmaceutical client, aligning hiring criteria, performance review rubrics, and development plans

• Partnered with executives to develop multi-state employee handbooks (NJ, FL, GA) for a shipping client, ensuring compliance with state-specific employment law

• Ran a workplace climate assessment and synthesized employee insights into actionable recommendations delivered to executive leadership

• Developed professional Human Resources expert task prompts to evaluate AI performance across workforce planning, talent analytics, policy drafting, and performance management scenarios

• Partnered with technical and operations teams to improve AI systems through HR subject matter and structured content evaluation

• Performed quality assurance and calibration of AI generated HR content by identifying reasoning gaps, validating instruction adherence, and refining prompts to produce measurable improvements in model performance

• Red teamed multimodal AI systems, surfacing failure modes across language, vision, and task execution

• Evaluated models for harmful outputs, policy violations, factual errors, and misuse risk across real-world scenarios

• Documented findings with clear reproduction steps and risk severity ratings to support researcher and QC review

• Partnered with research and engineering teams to improve model reliability, safety alignment, and data quality

B.A. in Human Resource Management & Labor Studies and Employment Relations – Rutgers University – New Brunswick (2025-05)