Machine Learning Data Engineering

ERIC RAVET

206-***-**** ****.*.*****@*****.*** linkedin.com/in/eric-ravet github.com/eravet

Versatile engineer with experience in data engineering, software development, and machine learning. Skilled in Python, SQL, and cloud-based data pipelines, with a track record of automating workflows, optimizing system reliability, and developing scalable solutions. Strong background in analyzing large datasets, building ETL pipelines, and evaluating model performance using statistical and machine learning techniques. EXPERIENCE

BattGenie Austin, TX

Software Engineer / Battery Test Engineer Mar. 2022 – Sept. 2023

• Maintained data pipeline to automate test data ingestion and transformation for large-scale battery performance data

• Logged and resolved data pipeline bugs, ensuring accurate and reliable performance evaluations

• Conducted performance analysis, delivering insights to R&D team for improved battery charging algorithms

• Created a standardized reporting package, cutting report creation time by 20% and improving data consistency True Brands Seattle, WA

Senior Demand Planner / Data Analyst Jul. 2013 – Oct. 2018

• Managed forecast models for 3,800 SKUs with 90% forecast accuracy during 5-year period of 20% YOY growth

• Reduced inventory costs by 26% and manual input by overhauling inventory ordering workflow

• Automated purchase order process which saved my team 20+ hours per month

• Designed an automated labeling system, reducing errors and saving 10+ hours per month

• Led cross-functional strategy meetings, translating data insights into actionable business decisions EDUCATION

University of Texas at Austin Austin, TX

Masters in Computer Science, Machine Learning Specialization Jan. 2022 – May 2024 University of Washington Seattle, WA

B.S. in Chemical Engineering with focus in French Sep. 2006 – Jun. 2011 PROJECTS

Object Detection with Convolutional Neural Network Python, PyTorch, OpenGL

• Designed and trained a fully convolutional neural network (FCNN) using binary cross-entropy to detect objects in a video game for AI-based decision-making

• Formulated an unproject transformation from 2D screen space to 3D game space, enabling teammate communication and puck trajectory estimation

• Fine-tuned model parameters using intersection over union (IOU) and precision-recall analysis SQUaD Dataset Improvements Python, PyTorch, Hugging Face

• Analyzed the Stanford Question-Answering Dataset (SQuAD) to identify and categorize common error patterns

• Leveraged Integrated Gradients to uncover why the model incorrectly prioritized specific keywords, pinpointing sources of misclassification

• Designed adversarial examples targeting these keywords, improving model accuracy by 10% on an error subset AWARDS

True Brands Employee of the Month Sep. 2018

US Fulbright Program Fulbright Research Fellowship Oct. 2011 – Aug. 2012 SKILLS & CERTIFICATIONS

Programming Languages: Python, SQL, Javascript

Libraries & Frameworks: Pandas, NumPy, PyTorch, SQLAlchemy, psycopg2, matplotlib, Flask Tools & Platforms: Git, VS Code, Jupyter Notebooks, Pytest Certifications: AWS Certified Cloud Practioner – Foundational (Feb. 2025) INTERESTS

Youth Mentoring, Endurance Cycling, Furniture Design & Crafting, Lacto-fermentation

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