Aniket Das

• Automated a financial reporting workflow using Python with 50+ business rules, cutting manual data processing by over 95%. • Developed a robust data pipeline to load, clean, and merge large-scale Excel reports, resolving complex formatting issues. • Reduced over 229,000 unclassified financial records to just 30, achieving a 99.97% fill rate, enabling focus on minimal exceptions. • Delivered a user-friendly Streamlit web app, enabling finance teams to execute the entire automation pipeline independently.

• Developed predictive models to forecast bugs in automation testing for a no-code platform, improving detection rates by 30%. • Optimized ML algorithms to identify high-risk areas in the codebase, reducing manual testing time and improving efficiency. • Analyzed historical bug data to enhance model accuracy, leading to a 20% reduction in post-deployment issues. • Integrated predictive analytics into testing workflows, streamlining bug tracking and resolution processes with the engineering team.

• Developed predictive models for CNC maintenance, reducing failures by 38% and improving overall production efficiency. • Predicted tool wear, extending tool lifespan by 12% and lowering downtime by 28%, leading to significant cost savings. • Analyzed machine performance data to optimize workflows, reducing production time by 18% while maintaining high-quality output. • Implemented predictive maintenance solutions, enhancing operational efficiency and minimizing unexpected disruptions.

• Processed 10,000+ velocity vectors from NACA0012 simulations, accurately identifying Kármán Vortex transitions with 95% precision. • Developed predictive models for reduced frequency estimation (k=1.82 to k=14.60) with 92% accuracy, reducing manual wake analysis by 30%. • Analyzed 30,000+ velocity data points, improving wake formation predictions using statistical and computational methods. • Applied AI-driven insights to Computational Fluid Dynamics (CFD), enhancing jet deflection and hydrofoil design efficiency.

• Gaining expertise in Machine Learning, Database Management, Data Mining, Probability, and Statistics to drive data-driven solutions. • Built ML & Deep Learning projects across multiple domains, including healthcare and construction management, applying AI to real-world challenges. • Developing advanced skills in data analysis, predictive modeling, and big data processing to optimize decision-making and automation.

• Worked on Hybrid Nanofluid heat transfer analysis and Low Clearance Go-Kart Racing Vehicles, applying engineering and computational analysis. • Secured 1st place in the Student Kart Design Challenge and won the Best Lightweight Vehicle Award at Kings Karting Championship for innovative vehicle design. • Participated in hackathons like NASA Space Apps Challenge, solving real-world problems through engineering and data-driven approaches.