ASHWIN BALDAWA

• PERL AI Platform
  Objective: Build a unified platform for Market Insights, Design Generation, and Sustaining Engineering.
  Approach: Integrated sentiment analysis, text-to-image generation, BOM-driven cost insights, and an agentic chat interface.
  Impact: Identified 20+ cost-saving opportunities; accelerated design feedback cycles by 30%.
  Tech Stack: OpenAI, Google Gemini, Azure Cloud, Azure AI Search, Azure SQL Server, Azure DevOps, FastAPI, React.
• Knowledge Procurement Systems AI (KPS AI)
  Objective: Deliver a scalable SCM platform for supplier and item categorization, KPI tracking, and risk profiling.
  Approach: Built KPS GPT, Magic Cube dashboards, and Supplier Chatbot for data harmonization and insights.
  Impact: Reduced Spend Pro dashboard preparation from 3-4 weeks to 1 day; improved analysis speed by 10x, supplier insight coverage by 60%.
  Tech Stack: OpenAI, Perplexity, Databricks, Azure Cloud, Azure AI Search, Azure SQL Server, Azure DevOps, FastAPI, React.
• SOP AI
  Objective: Provide consultants with structured SOP data via chatbot.
  Approach: Fine-tuned LLaMA models on AWS SageMaker and Bedrock, integrated with Python, Redis, and SQL Server for dynamic data retrieval.
  Impact: Enabled real-time access to internal SOP knowledge, reducing search time by 40%.
  Tech Stack: LLaMA, SageMaker, Bedrock, Python, Redis, SQL Server.

• Trade Promotion Optimization
  Objective: Optimize trade promotion planning for consumer goods.
  Approach: Developed ML algorithms for promotion mix optimization.
  Impact: Achieved 12% sales uplift, 3% market share growth, ROI boost from 9% to 13%.
  Tech Stack: Python, ML algorithms, Azure, Power BI.
• Gen AI-based Diligence
  Objective: Automate forensic auditing of annual reports.
  Approach: Built Gen AI tool leveraging LLMs and RAG for rapid discrepancy detection.
  Impact: Reduced manual effort by 70%, improved accuracy and detail in identifying cash flow patterns and discrepancies.
  Tech Stack: Python, OpenAI, AKS, Power BI.
• Net Revenue Management (NRM) Insights
  Objective: Drive revenue growth through analytics.
  Approach: Built analytics tools using multiple levers (Trade, Price, Promo, PPA).
  Impact: Delivered $22M in revenue growth.
  Tech Stack: Alteryx, Python, Databricks, AWS, Power BI.

Senior Data Scientist & Data Engineer
Tata Consultancy Services | Oct 2021 – Sep 2022

• Product Harmonization
  Objective: Harmonize manufacturer and retailer SKUs.
  Approach: Developed E2E SKU harmonization solution.
  Impact: Delivered $1.5M in cost savings.
  Tech Stack: AWS, Databricks, PySpark, Redshift, Power BI.

• Automated Text Extraction
  Objective: Automate extraction of text from customer forms.
  Impact: Reduced manual data entry by 80%.
  Tech Stack: Python, ML models.
• ML-based Credit Card Issuance Engine
  Objective: Optimize credit card approval.
  Impact: Improved approval rates, reduced CAC, increased market share.
  Tech Stack: Python, ML, SQL.

• Developed end-to-end credit card processing pipelines, including data verification, scoring models, and ETL automation.
• Streamlined background checks and improved processing efficiency across customer on boarding workflows.