Supply Chain United States

VARUN VELANKAR

+1-215-***-**** *******@****.*****.*** https://www.linkedin.com/in/varun1203/ Philadelphia, PA Skills

Programming Python, R, SQL, Matlab, LabVIEW, C/C++, Javascript, Git, Github, Bash, Powershell, Linux, Unix Software Advanced Excel, Tableau, PowerBI, Capital One Slingshot, UIPath, Apache Spark, Alteryx, AWS(S3, Lambda, Redshift) Technical Data Warehousing, ETL Pipeline, Unstructured Data handling, Data Quality Management, SPC, Agile, Lean, Six Sigma, Database Management, Kaizen, Data Mining, PFMEA, Data Cleaning, Data Wrangling, Process Improvement Consulting Risk Management, Statistical Model Building, Financial Modeling, KPI Identification, Product Strategy, Marketing Experience

ASML, Data and Process Insights Analyst (SQE) Wilton, Connecticut, United States May 2024 August 2024

• Achieved 12% supplier defect reduction by developing real time supply chain dashboards using SQL, Python Pandas, and Power BI

• Delivered $1.4M year on year cost savings through root cause analysis with Scikit Learn Random Forest models and SQL

• Shortened report generation by 60% by building automated ETL pipelines with Airflow, AWS Lambda, and Redshift

• Boosted process yield by 7% via SPC chart integration in Tableau, driving continuous improvement in supplier quality Coursera, Software Engineering TA PA, United States May 2024 Present

• Reduced CI/CD feedback loop by 40% by implementing GitHub Actions pipelines using Docker and PyTest for automated test suites

• Boosted grading efficiency by 30% using a cloud based autograder in AWS Lambda, FastAPI, and DynamoDB to process submissions

• Reduced assignment resubmission rate by 25% by performing cluster analysis on student error patterns using Python libraries

• Increased student engagement by 15% via real time performance analytics built with Flask and DataDog for in class coding projects Wharton Business School, Data Analyst Banking & Consulting PA, United States August 2023 May 2024

• Increased forecast accuracy by 35%by implementing ARIMA time seriesmodelsinPython&R for investment banking risk assessment

• Boosted pilot adoption by 30% by developing a comprehensive go to market strategy encompassing pricing analysis, distribution channel segmentation, and stakeholder alignment using Excel VBA, PowerPoint, and Tableau data dashboard integration

• Reduced reporting cycle by 52% by creating self serviceBI dashboards with Tableau and Power BI that tracked P&L and KPI metrics

• Generated $500K in revenue opportunities by delivering M&A valuation models in Python (NumPy, Pandas) for due diligence Education

3.35/4.0 Master of Science in Systems Engineering, University of Pennsylvania PA, USA 2023 25 3.9/4.0 Bachelor of Technology in Mechatronics Engineering, MIT World Peace University Pune, India 2019 23 Courses: Data Science, Artificial Intelligence, Operations Research, Supply Chain Management, Statistical Analysis, Machine Learning, Neural Networks, Databases, Digital Signal Processing, Optimization, Mechanical Design, Control Systems, 3D Printing, Finance Publications & Projects

Independent Study EUV Semiconductor Competitive Strategy Jan 2025 Present Wharton Business School

• Analyzing EUV lithography ecosystem by evaluating 50+ patent citations, supplier dependencies, and competitive advantages to as sess technological leadership and barriers to entry in semiconductor manufacturing

• Conducting an in depth analysis of ASML’s EUV lithography dominance, assessing $25B+ in capital investments, supply chain depen dencies, and geopolitical risks to evaluate its long term competitive advantage in semiconductor manufacturing Signal Processing Adaptive Filter Design Sept 2023 December 2023 Penn ESE Digital Signal Processing

• Achieved 40% faster convergence rate by implementing real time adaptive filter algorithms (LMS, RLS) in MATLAB, optimizing system identification and frequency domain response through closed loop coefficient tuning and feedback control

• Reduced noise interference by 85% in complex sinusoidal signals through design of adaptive FIR/IIR notch filters with gradient based optimization, utilizing transfer function analysis and Bode plot characterization CycleGAN Image Translation – Model Deployment & Data Quality Automation January 2024 May 2024 Penn Department of Computer Science

• Reduced model artifacts by 35% using a CycleGAN for domain to domain image translation across 15K+ samples, improving data pipeline consistency for downstream classification

• Logged model performance metrics (FID, PSNR) and pipeline outputs for auditability using TensorBoard and monitoring scripts Graph Neural Network – Recommender System Sept 2023 December 2023 Penn Department of Computer Science

• Boosted recommendation precision by 28%by engineering a GraphNeuralNetwork(GraphSAGE) model on 250K+ user item records, applying collaborative filtering in Python

• Enabled reproducible self servicemodelingbymaking end to end ML lifecycle from feature transform to validation with AUC > 0.91

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