Prabudh Sharma

Detail-oriented Data Analyst with over 4 years of experience specializing in data integrity, statistical analysis, and automated quality assurance. Currently serving as a Business Analyst at Turing, where I lead high-priority data auditing for Google DeepMind's LLM projects. My expertise lies in performing Side-by-Side (SxS) evaluations, rigorous fact-checking, and identifying complex data anomalies to ensure model grounding and accuracy.

Previously, I spent 3 years in mechanical manufacturing, where I leveraged SQL, Python, and statistical modeling to optimize production workflows and reduce defect rates through outlier detection and variance analysis. I possess a strong technical foundation in Excel, Tableau, and data aggregation, with a proven ability to translate complex datasets into actionable business insights.

Business Analyst (AI Data Quality) at Turing (Google DeepMind Project) (2026-03 – Present)

Perform high-volume Side-by-Side (SxS) evaluations on GHD to benchmark LLM outputs for accuracy, relevance, and grounding. Execute rigorous fact-checking workflows to identify data inconsistencies, hallucinations, and subtle inaccuracies in AI-generated datasets.

• Perform high-volume Side-by-Side (SxS) evaluations on GHD to benchmark LLM outputs for accuracy, relevance, and grounding
• Execute rigorous fact-checking workflows to identify data inconsistencies, hallucinations, and subtle inaccuracies in AI-generated datasets
• Author detailed, data-backed justifications for evaluation decisions, ensuring high-quality training data for model optimization
• Utilize browser-based diagnostic tools and command-line interfaces (CLI) to navigate complex evaluation workflows and audit data integrity
• Monitor and document data trends, ensuring all outputs align with strict project-specific quality standards and technical guidelines
• Collaborate with cross-functional teams to provide actionable insights on model performance and edge-case anomalies

Quality Controller at Quality Control Department (2019-01 – 2022-12)

Performed statistical analysis to evaluate mechanical part quality and process performance. Applied regression techniques to analyze production data and identify trends.

• Performed statistical analysis to evaluate mechanical part quality and process performance
• Applied regression techniques to analyze production data and identify trends
• Conducted standard deviation and variance analysis for tolerance optimization
• Implemented outlier detection methods to identify defective components
• Defined tolerance limits using median and statistical distribution models
• Supported quality assurance through data-driven inspection reporting
• Collaborated with production teams to improve manufacturing accuracy
• Maintained and analyzed quality datasets for process improvement initiatives

Certification in Data Analytics and Gen AI – Indian Institute of Technology Kanpur (2025-03 – 2026-03)

B.Tech in Mechanical Engineering – SRM Institute of Science And Technology (2015-01 – 2019-12)