Keerthana Kandasamy

About Keerthana Kandasamy

Orchestrating Intelligent Supply Chains at the Intersection of AI and Global Logistics.I have always been fascinated by the \"hidden\" puzzles that keep the world moving. Why does a product reach a shelf exactly when it’s needed? How do we prevent waste in life sciences? These questions led me to pursue a dual-path academic journey: an MBA in Analytics and a Master’s in Applied Machine Intelligence, giving me a unique vantage point to solve supply chain complexities.As a Certified Kinaxis Maestro Author (Level 1 & 2), I don’t just use tools; I design solutions. With 4+ years of experience across global leaders like Amazon, HCL, and LTIMindtree, I have made it my mission to turn \"dirty data\" into actionable supply chain intelligence.What I bring to the table:• Deep Kinaxis Expertise: From configuring complex BOM Explosions and Lead Time logic to implementing Inventory Optimization (IO) and Safety Stock strategies that actually work.• The AI Edge: My background in Machine Intelligence allows me to look beyond standard heuristics. I specialize in integrating Statistical Forecasting and Predictive Modeling into the planning cycle to reduce MAPE and increase service levels.• Impact-Driven Mindset: Whether it is saving $750K/year through premium rating models or reducing excess inventory by 12%, I focus on the bottom line.• Beyond the Data: I am a firm believer that the best supply chains are built on collaboration. I thrive in cross-functional environments where I can translate technical \"geek-speak\" into clear business strategy for stakeholders. Let’s connect if you want to talk about: Kinaxis Maestro/RapidResponse implementations The future of AI-driven Demand & Supply Planning Solving the \"impossible\" puzzles of Master Data and Inventory strategy

Experience

1. Supply Chain Data Engineer (Ml Da Ii)

   Amazon

   May 2025 — Present · Boston, MA, US

   Operational Intelligence: Leveraging Generative AI to optimize Supply Chain Intelligence frameworks, focusing on the evaluation of LLM outputs to identify and correct Master Data inconsistencies. • Predictive Quality: Engineered prompt optimization techniques to reduce noise in high-volume data streams, ensuring the accuracy of Demand Signals and reducing latency in operational decision-making. • Root Cause Analysis: Collaborating with SMEs to perform RCA on data defects, implementing automated corrective process improvements to enhance the reliability of Predictive Modeling systems.

Education

• Northeastern University

  Master of Professional Studies, Applied Machine Intelligence

• SRM Institute of Science and Technology (SRMIST)

  Bachelor of Technology - BTech, Computer Science and Engineering

• SRM Institute of Science and Technology (SRMIST)

  Master of Business Administration - MBA, Analytics