Welcome to Harsha's notes on data science.
Harsha Achyuthuni is a seasoned data science professional with over 10 years of experience, currently serving as a Manager at Deloitte. He likes solving complex business problems by integrating data, statistics, technology and business understanding.
With a Master’s in Business Analytics from Imperial College London and executive education from IIM Bangalore, Harsha has consistently delivered impactful AI and ML solutions across diverse industries, including aerospace, FMCG, pharmaceuticals, insurance, retail, agriculture and manufacturing. His expertise spans predictive maintenance, demand forecasting, optimisation engines, supply chain analytics and cutting-edge generative AI applications.
Throughout his career, Harsha has led high-impact projects for global clients such as Rolls-Royce, PepsiCo, Walmart and Dr. Reddy’s, Tata AIA Life Insurance, and Clarus One. His work includes building scalable MLOps pipelines, deploying physics-informed ML models, and developing GenAI tools that streamline operations and enhance decision-making. Notably, his predictive models have influenced multi-million-pound sourcing decisions and saved thousands of man-hours through intelligent automation. He is also a published researcher and award-winning presenter in the field of advanced analytics.
Beyond his technical acumen, Harsha is a recognised thought leader and innovator, having received accolades such as Deloitte’s President’s Bonus, Best Paper Award at the International Conference on Business Analytics, and multiple client appreciation awards. Passionate about bridging business and technology, he continues to explore the frontiers of AI, sharing insights through his website harshaash.com and contributing to the global data science community.
Some of Harsha's interesting projects are:
Explored unsupervised learning methods to predict aircraft engine part failures, led the deployment of physics-informed ML predictive maintenance models for two parts across 1000+ engines
Developed a GenAI-based tool to automate the extraction of key data from US Government pre-solicitation and solicitation bids, accelerating bid creation for sales and supply chain teams
Improved under-stocking for Walmart’s LATAM supply chain, delivering $1.2M/month in savings by modelling supplier delivery risk, forecasting lead times, and suggesting an optimised EOQ/reorder points that integrate these risks via integer programming and Monte Carlo simulations
Built and deployed granular volume forecasting models across the North American market using Python, PySpark, and Databricks, achieving 90% accuracy and improving planning across customer, location, and package dimensions
Developed a competitive intelligence pipeline leveraging FDA inspection news via Google News API and web scraping, integrated with PaLM 2 for analysis, and deployed using Airflow, Vertex AI, and BigQuery on Google Cloud Platform
Collaborated on data science initiatives to optimise store performance and operations across Tesco’s UK, Central Europe, and Ireland markets in the Space, Range, Display, Merchandising, and Promotions domain
Created a “Human with AI” tool that saved 1,200+ man-hours by assisting SMEs in diagnosing engine issues
Developed a proof of concept (PoC) application that assigned jobs in a manufacturing plant based on contractor preferences, past performance and availability
Built an NLP-based PoC to estimate ingredient composition and cost in food items using USDA nutrient data, aiding sourcing managers and food scientists in procurement decisions
Set up a Data Centre of Excellence using Azure Cloud and built scalable data pipelines
Developed a supplier optimisation engine that influenced £180M in sourcing decisions
Created "Rewards and Returns" contests for an insurance client to motivate agents to perform better and increase efficiency