Himanshu Tiwari

WORK HISTORY

EDUCATION

ABOUT HIMANSHU TIWARI

I\'m a Data Scientist at WMG, University of Warwick, working on AI-driven solutions to improve forecasting, predictability and operational performance across supply chain systems. My work sits where research meets industry, building models that hold up in messy, real-world operational environments. My path into data wasn\'t a straight line. I started in mechanical engineering, moved through manufacturing and production planning at Bosch, and came to analytics through the problems I kept running into on the shop floor: demand variability, inventory blind spots, forecasting that didn\'t quite hold. An MSc in Business Analytics at Warwick gave me the technical foundation to tackle those problems properly, and I\'ve been at that intersection of operations, data and ML ever since. A few things I\'m proud of: • At Bosch India, I led daily production planning and demand forecasting for after-market supply, contributing to a 100% fulfilment rate and supporting a 20% reduction in quality-related defects through data-driven root-cause analysis. • At iHub-Drishti (IIT Jodhpur), I built clustering-based models that automated financial audit classification, cutting audit cycle time by ~30% across 30+ client engagements. • At Adstory, I analysed 100k+ row datasets to surface retention, conversion and spend-efficiency drivers, building Python/SQL pipelines and Power BI dashboards that directly shaped campaign decisions. Alongside client work, I\'ve been deepening my ML and GenAI skills through projects, including a Retrieval-Augmented Q&A system (LangChain, Gemini Pro, RAG), an end-to-end MLOps pipeline for phishing detection (MLflow, schema validation, automated evaluation), and an abstractive text summarisation system using fine-tuned Transformers. Building these end-to-end is where I get to explore the parts of data science I find most exciting: the intersection of modelling, engineering and real-world application. What I\'m looking for: roles in data science, supply chain analytics, applied ML, BI or data engineering where I can combine operational context with technical depth, ideally somewhere that values both the why and the how. Always up for a conversation, whether it\'s a role, a collaboration, or just comparing notes on an interesting problem. Reach me at h••••••••@gmail.com or on LinkedIn.