# yaml-language-server: $schema=https://github.com/rendercv/rendercv/raw/main/schema.json
# PerfectCV sample CV in YAML format (public-safe, fictional). You can edit it to create your own CV.

cv:
  name: John Doe
  headline: AI/ML Intern and Junior Data Scientist
  location: Austin, TX (open to remote, EU relocation; work authorization required)
  email:
    - john.doe@example.com
    - jdoe@university.example.edu
  phone: +1-202-555-0143
  website: https://johndoe.example.dev/
  social_networks:
    - network: LinkedIn
      username: johndoe
    - network: GitHub
      username: johndoe
  sections:
    Summary:
      - >-
        Data Science and Artificial Intelligence student at Example University,
        Faculty of Engineering (2024-2027, GPA 3.7/4.0). I design, train and
        ship end-to-end machine learning systems, from data pipelines and model
        training to deployment and dashboards. Core stack: Python, PyTorch,
        scikit-learn, SQL and C++, with strong foundations in statistics,
        probability and A/B testing. Seeking AI/ML intern and Junior Data
        Scientist roles (EU + remote).
    Education:
      - institution: Example University, Faculty of Engineering
        area: Data Science and Artificial Intelligence
        degree: BSc
        start_date: 2024-09
        end_date: 2027-06
        location: Austin, TX
        highlights:
          - "GPA 3.7/4.0. Degree in progress, plan to pursue Master after Bachelor."
          - "Relevant: Machine Learning, Artificial Intelligence, Algorithms and Data Structures, Statistics and A/B testing."
      - institution: Austin Technical High School
        area: Information Technology
        degree: Technician
        start_date: 2020-09
        end_date: 2024-06
        location: Austin, TX
        highlights:
          - "Foundation in programming, computer networks and hardware labs (Python, HTML/CSS, Arduino)."
    Experience:
      - company: Student Developers Club
        position: ML Project Maintainer (Volunteer)
        start_date: 2025-01
        end_date: present
        location: Austin, TX
        highlights:
          - "Maintain club project index and review beginner pull requests alongside studies."
    Projects:
      - name: ChurnPredictor - Customer Churn Classifier
        summary: |
          Python, PyTorch, scikit-learn, Pandas. Course Machine Learning, Example University. Team of 3.
          [Code](https://github.com/johndoe/ChurnPredictor) [Demo](https://example.com/demo)
        highlights:
          - "My role: trained the classifier on the dataset teammates prepared; built the evaluation notebooks and the results dashboard."
          - "Benchmarked 4 models (logistic regression, random forest, MLP, GRU) on 12 months of subscription events reduced to 8 features; exported the winner for batch inference."
          - "Result: 91% accuracy on 8,400 test rows (macro F1 0.90); weekly retraining pipeline."
      - name: StreetSigns - Traffic Sign Detection
        summary: |
          Python, YOLO, OpenCV. Course Data Science and AI, Example University. Team of 2.
          [Code](https://github.com/johndoe/StreetSigns)
        highlights:
          - "Trained a small detector on 4,200 dashcam frames (9 sign classes) and compared two model sizes in notebooks."
          - "Best: P80.2 R70.1 mAP50 74.0; documented training curves and failure cases in the repo."
      - name: ShopRecommender - Graph Recommender
        summary: |
          Python, Flask, NetworkX, Pandas. Course Algorithms and Data Structures, Example University.
          [Code](https://github.com/johndoe/ShopRecommender) [Demo](https://example.com/shopdemo)
        highlights:
          - "Built a user-product bipartite graph recommender with Jaccard similarity on 20k ratings from 300 users."
      - name: MazeRunner - C++ Game AI
        summary: |
          C++17, CMake. Course Artificial Intelligence, Example University.
          [Code](https://github.com/johndoe/MazeRunner)
        highlights:
          - "Implemented Minimax with alpha-beta pruning for a grid strategy game with 3 difficulty levels."
          - "Tested on 6x6 and 8x8 boards: easy mode beatable, hard mode wins most games."
    Skills:
      - label: Programming
        details: Python, C++, HTML/CSS, SQL, JavaScript. Full live list on portfolio.
      - label: AI/ML
        details: PyTorch, scikit-learn, OpenCV, CNN/GRU, Minimax, graph recommendation
      - label: Data Science
        details: Pandas, NumPy, data cleaning, feature engineering, evaluation (accuracy, precision, recall, F1, mAP), statistics, probability, A/B testing
      - label: Engineering
        details: Flask, Git/GitHub, Docker, Jupyter, REST APIs
    Languages:
      - label: Languages
        details: English (Native), Spanish (Intermediate)
    Interests:
      - Autonomous systems, game AI, recommendation systems. See live work at [portfolio](https://johndoe.example.dev/).
design:
  theme: classic
  page:
    size: a4
    top_margin: 0.7in
    bottom_margin: 0.7in
    left_margin: 0.7in
    right_margin: 0.7in
    show_footer: true
locale:
  language: english