CV

General Information

Full Name Tyler Binning
Email tybinning43@gmail.com

Education

  • 2025
    Bachelors of Science
    Brigham Young University - Idaho, Rexburg, Idaho
    • Vice President, Data Science Society
  • 2022
    Dual Enrollment Coursework
    Southwestern Community College, Osceola, Iowa
    • Completed college-level coursework in mathematics and general education while attending high school through a dual enrollment program.

Experience

  • 2025
    Data Science Internship
    Planck AI
    • Led full-cycle AI development, from data extraction and cleaning to transformer fine-tuning (Phi-2, T5) with LoRA and RAG, boosting accuracy and contextual reasoning.
    • Built scalable ETL and retrieval systems, converting legal contract data into JSONL and integrating custom vector stores and prompt templates for efficient document search.
    • Deployed models in an interactive Streamlit app, enabling real-time document classification, clause tagging, and analytics visualization.
    • Advanced model performance through research and collaboration, applying TF-IDF and Logistic Regression benchmarks, optimizing hyperparameters, and standardizing labeling and evaluation protocols.
  • 2025
    AI Engineering Consultant
    Brigham Young University - Idaho
    • Collaborated within a team to design and develop an AI-powered Teaching Assistant, integrating generative AI to provide course guidance and answer student questions.
    • Integrated course materials into the AI Teaching Assistant using a Retrieval-Augmented Generation (RAG) model, ensuring accurate, context-aware responses to enhance student learning.
    • Worked with faculty to refine AI-generated insights, ensuring alignment with academic standards and improving instructional effectiveness.

Certificates

  • 2025
    Google Advanced Data Analytics Professional Certificate
    Google
    • Statistical analysis & hypothesis testing: applied inferential statistics, ANOVA, and experimental design for data-driven decision making.
    • Regression & predictive modeling: built and evaluated linear/logistic regression, forecasting models, and supervised ML algorithms.
    • Machine learning fundamentals: implemented clustering, classification, and ensemble methods in Python.
    • Programming in Python: applied pandas, NumPy, scikit-learn, and Jupyter Notebooks for data wrangling, analysis, and modeling.
    • Data visualization & storytelling: created effective dashboards and visualizations with Tableau and Python libraries (matplotlib, seaborn).
    • Data wrangling & cleaning: transformed messy datasets into structured, analysis-ready formats.