Senior analytics leader building enterprise AI and ML platforms.
Senior Analytics Manager at Caterpillar.
I have 12 years of experience building enterprise AI and ML platforms. For seven of those years I have led global data science and engineering teams, three of them as a leader of leaders.
My work sits where analytics meets big operational decisions: what a company buys, which suppliers might fail, and how to plan for parts years ahead.
An agentic AI platform built on knowledge graphs, with a suite of ensemble models, to optimize $25B of direct procurement spend.
$300M+ in cost reduction opportunities identified and $25M+ in savings realized.
Time series, tree-based and transformer models monitor 7K suppliers and 800K parts across 63 countries to predict supply disruptions. I tuned recall and precision deliberately, so planners would trust the alerts and use them.
Captures more than 70% of potential disruptions with 50% lead accuracy.
The company’s first generative AI platform, built on retrieval-augmented generation with LangChain, a vector database, a custom multi-modal extraction pipeline and human-in-the-loop validation.
Processes 100K+ contracts and extracts 150+ elements, cutting the work from weeks to minutes.
Extended reliable planning signals across 800K active parts.
Coverage rose from 32% to 99% of spend ($22B).
A single, safe, consumption-ready source of data and standardized metrics for the analytics teams.
Accelerated downstream analytics by an estimated 30%.
I lead a global analytics team of 14: two analytics managers, a project manager, six data scientists and five data analysts. I own strategy and execution for enterprise AI in procurement, cost and risk analytics, and I recruit, mentor and develop the team through the full lifecycle of each ML product.
I was asked to advance analytics capabilities in India and built that organization from the ground up. I also mentor a class of 12 in AI at Purdue University through the Data Mine program, a partnership to develop future talent.
I led projects at Fortune 500 companies as a data science and engineering consultant. I led a team of 21 data scientists, engineers and developers on eCompass, a supply chain ML platform that predicts sales and optimizes supply chains, and reduced out-of-stock incidents by 6%.
I also designed an omni-channel data science as a service platform for personalized content, with automated A/B testing, customer segmentation and natural language generation. It cut the timeline for a new marketing campaign from 120 days to under 30 and improved click rates by 30%.
I led five data scientists building DBLytix, an in-database analytics library, from algorithm development through packaging, testing, documentation and deployment. Multiple general availability releases on Teradata, Netezza and Aster generated more than $2M in revenue.
I also built fintech analytics for capital market index calculation and rebalancing, led client engagement and cloud deployment in Hong Kong, and trained six new hires and nine interns.
The best ways to reach me are email and LinkedIn.