Shreydhar Data Professional

Transforming raw data into actionable insights

Career Overview

Total Experience

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Highest Education

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Total Projects

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Certifications

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Skills vs % Projects
Skills vs % Certificates
Industry vs % Projects
Industry vs % Certificates
Key Business Deliveries
  • BFSI (Fraud & Pricing Optimization):
    Delivered $2M+ in client savings by deploying NLP-based fraud detection (LLMs/Transformers) for bank statements and postpaid bills, reducing manual review time by 40%. Built time-series models (ARIMA/Prophet) for inventory forecasting, cutting costs by 20% and improving logistics TAT by 33% (6h → 4h). Relevance: Directly aligns with mortgage pricing risk modeling and automated decision-making.
  • Healthcare (Due Diligence for Investors):
    Led end-to-end analytics for a digital healthcare provider (2B+ records/year), identifying synthetic behavior and operational risks. Delivered executive-ready insights that quantified risk exposure, enabling $10M+ in cost avoidance for investors. Relevance: Proves large-scale data validation—critical for mortgage data integrity.
  • Manufacturing (Process Mining & Logistics):
    Optimized Gate In/Out logistics for a global cement client using process mining (MPM/Knime), improving efficiency by 25–35% and reducing manual intervention. Built ARIMA/Prophet models for demand forecasting, saving $1.2M annually in inventory costs. Relevance: Demonstrates time-series forecasting—core to mortgage pricing models.
  • Oil & Gas (Anomaly Detection & Platforms):
    Developed FAE Platform (Streamlit, MSSQL, SageMaker) with role-based access, automated ETL, and real-time dashboards, improving processing efficiency by 40% and reducing deployment time by 30%. Detected 4–6% risky coupon codes (400K records), preventing $500K+ in fraud losses. Relevance: Showcases scalable ML platforms—applicable to mortgage fraud prevention.
  • Marketing (Customer Analytics):
    Designed a diagnostic engine for 50M+ email records, optimizing customer opt-ins and reducing churn by 10% via predictive propensity models. Increased client profitability by 12% through personalized rewards. Relevance: Highlights customer behavior modeling—useful for mortgage segmentation.
  • Flavor & Fragrance (Predictive Sales):
    Built XGBoost/Random Forest models to predict project success, improving win rates by 15%. Optimized supply chain costs via route/lead-time models, preventing stockouts/overstocking. Relevance: Proves predictive modeling—transferable to mortgage pricing optimization.
  • Public Sector (IoT & Fleet Optimization):
    Deployed Genetic Algorithms/Dijkstra’s for real-time route optimization, cutting fuel costs by 20% and delays by 15%. Developed LSTM-based predictive maintenance, reducing downtime by 30%. Relevance: Validates real-time analytics—key for dynamic mortgage pricing.

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