Data Analyst Machine Learning

Antoine Pangas

*******@*****.*** 202-***-**** Washington D.C.

EDUCATION

University of Michigan - College of Engineering Dec. 2020 - Dec. 2024 B.S.E. Computer Science, GPA: 3.52 / 4.0 Ann Arbor, MI

Relevant Coursework: Data Structures and Algorithms, Database Management (SQL), Object-Oriented Programming, Computer Networks and Communication, AI and Machine Learning, Natural Language Processing, Web Development, Discrete Math, Linear Algebra, Statistics, Derivatives and Options Pricing PROFESSIONAL EXPERIENCE

Pactiv Evergreen Inc. Jun. 2024 - Aug. 2024

Data Analyst Lake Forest, Il

Developed and deployed data processing pipelines in Python, integrating APIs and SQL for automation, reducing manual processing time by over 75%

Designed and implemented real-time Power BI DAX and Azure Data Factory for KPIs, reducing reporting time by 50% and enabling daily monitoring of $50M+ in shipments across 20+ distribution centers.

Led Agile development of comprehensive data documentation and quality control systems, laying the groundwork for ML applications in demand forecasting.

Researched AI applications in production as part of an internal task force and presented key findings to senior management.

TriplePoint Capital LLC May. 2022 - Aug. 2022

Software and Data Analyst Menlo Park, CA

Optimized SQL queries with indexing and materialized views, reducing response times by 40%. Built scalable data pipelines to automate financial data processing.

Built financial analysis models using regression techniques and Monte Carlo simulations to compare company metrics against industry benchmarks, ultimately supporting three venture debt investments.

Created automated dashboards using Tableau to analyze 1200+ investments, enabling data-driven decision making for portfolio management.

PROJECTS AND ADDITIONAL EXPERIENCE

ML-Based Quantitative Trading System

Developed an automated trading system integrating TSF deep learning and WebSocket APIs.

Achieved 1.5 Sharpe, 85% YoY returns, and 12% max drawdown across multiple market regimes.

Engineered robust feature pipeline processing real-time market data using NumPy vectorization, implementing custom technical indicators to reduce noise resulting in a 72% overall prediction accuracy.

Created and deployed a distributed web scraping system using Tesseract on Heroku that streams real-time market sentiment from 50+ sources.

Implemented ML ensemble architecture integrating price action, volatility, and portfolio correlation to dynamically adjust position sizing and risk parameters, resulting in 40% reduction of tail risk events. Distributed Search Engine with MapReduce

Engineered a scalable search engine with segmented inverted indexing via MapReduce and link analysis through PageRank.

Developed full-stack search interface using React and Flask, implementing API endpoints for real time querying and result pagination, achieving sub-200ms response times on 500MB+ datasets.

Built caching layer using Redis to optimize frequent queries, reducing average server load by 30%.

Implemented Python, Hadoop Mapreduce, and Service-Oriented Architecture for rapid performance and precise search results.

TECHNICAL SKILLS

Languages: Python, C++, JavaScript, SQL, R, BASH, MATLAB, Java

Libraries/Frameworks: Numpy, Pandas, Pytorch, Keras, Vertex, TensorFlow, React.js, Flask, Jupyter, Pytest, JUnit, Apache Spark, Google Test, AWS

Developer Tools: Git, Ubuntu, Cloud Computing Services, SAP, Docker, Power BI, SSH LANGUAGES AND INTERESTS

Languages: French, Spanish, Greek

Interests: Rock Climbing, Trading, Chess, Kiteboarding

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