Dr. Gyanesh Jain

Designed and implemented an IoT-enabled CIP Diagnostics system, projected to save $10 per wash across 4 million washes, optimizing chemical usage and energy consumption in the food and beverage industry.

Engineered a customer retention initiative leveraging a Markov model, which reduced Mean Absolute Percentage Error (MAPE) from 98% to 10% and significantly boosted customer engagement.

Drove a 1.5x increase in sales by conceptualizing and implementing a Best-in-Class Product Recommendation system, integrating RFM analysis and Meta LLAMA 40B for enhanced automation.

Achieved an estimated Net Present Value (NPV) of $80M by leading a team of six to develop a Guided Selling solution, utilizing K-means clustering and CNN-based multi-label prediction with 95% hit ratio for top 3 products.

Reduced false positives by 80% in an IoT-driven model through anomaly detection techniques, leading to a 50% increase in digital product sales.

Automated PowerPoint generation and meeting minute extraction using Databricks-integrated AI agents, streamlining workflows and enhancing team productivity.

Developed a Smartpaper solution for student assessment, processing 6 million scanned answer sheets daily, integrating multimodal LLMs for subjective response evaluation.

Optimized image processing with dewarping and deblurring techniques, reducing model inference time to ~1 second and cutting project costs by 50% through lightweight model development.

Deployed a Retrieval-Augmented Generation (RAG) UI via LangChain on AWS, resulting in a 40% cost reduction for a publishing house client.

Built custom frameworks for testing the reliability and integrity of assessment data from multiple states, utilizing R and Shiny Dashboards for comprehensive data exploration and reporting.

Designed and implemented an interactive Shiny dashboard for National Achievement Survey (NAS) data, consolidating diverse datasets for effective visualization and decision-making.

Delivered comprehensive training sessions on Data Science and Machine Learning concepts to corporate employees across various organizations, enhancing their technical capabilities.

Designed, developed, and evaluated specialized upskilling courses for working professionals, ensuring practical application of advanced analytical techniques.

Trained teams in core ML tools including Scikit-learn, SciPy, Pandas, and NumPy, fostering a strong foundational and applied understanding of data science methodologies.

Supported a strategic shift from made-to-order to made-to-stock for B2B sales by leveraging data-driven insights and developing algorithms to optimize product selection and inventory decisions.