Data Scientist & Engineering Supervisor specializing in end-to-end ML solutions. I design, develop, and deploy AI systems that optimize production and inspection workflows. Experienced in leading cross-functional teams, managing resources, and aligning AI initiatives with business goals. Skilled in MLOps, Deep Learning, Computer Vision, and Solution Architecture.

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Projects

Deep Reinforcement Learning for resource allocation in wireless networks

This project utilized Deep Reinforcement Learning (DRL) and Transfer Learning (TL) for power allocation in wireless cellular networks through simulation environments using Pytorch. This project resulted in three published refereed articles in top journals from Q1 and Q2 in the computer science area.

Recognition of eye diseases with neural networks

This project involves the development of a convolutional neural network (CNN) for detecting ocular diseases using the ODIR-5K database from the Kaggle platform. The CNN was designed using Python with Keras. The model achieved an accuracy of 89.2% in detecting various ocular diseases such as diabetic retinopathy, cataracts, glaucoma, and age-related macular degeneration.

San Diego home price prediction

This project involved collecting data through web scrapping, cleaning and processing data, and training a regression model to predict the home price based on the apartment's neighborhood, square feet, and the number of beds and baths. Finally, the model was hosted on a web page.

Explainable AI (XAI) for beer brand classification

This project combines a Convolutional Neural Network (CNN) based on the VGG16 architecture with GRAD-CAM, an explainable AI method, to interpret the model's focus for beer brand classification. The CNN is fine-tuned with additional layers, achieving an accuracy of 91.6%. Data preprocessing and augmentation were carried out using libraries such as Keras and Sklearn.

Bellabeat capstone project with R

This capstone project is part of the Google Data Analytics Course. The project consists of answering a business task based on the data provided. The data cleaning and data visualization was performed in R.

Sales analysis with pandas

This project analyzes a Kaggle database of electronic devices. The analysis involved cleaning the data and creating visualizations to answer important business questions.

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Career Overview

Alexis Anzaldo

SKILLS

• Programming: Python (TensorFlow, PyTorch, Keras, Scikit-Learn, OpenCV, Pandas, NumPy, Matplotlib, Seaborn), SQL.

• AI/ML Techniques & Models: CNN, YOLO, Transfer Learning, Reinforcement Learning, Explainable AI (GRAD-CAM), Data Augmentation, Outlier Detection, Regression, Large Language Models (GPT, Claude — fine-tuning, embeddings, prompt engineering)

• Tools & Platforms: Dagster, FastAPI, MS Project, Git, Power BI, Matlab, Labview.

• Spanish: Native. English: B2.

EXPERIENCE

• Lead AI and computer vision projects, aligning solutions with strategic business objectives.

• Coordinate resources, engage stakeholders, and oversee cross-functional teams to ensure timely delivery of AI initiatives.

• Guide the development of PoCs and prototypes, driving adoption of innovative AI solutions in manufacturing workflows.

• Mentor and support team members while balancing technical execution with administrative responsibilities.

• Developed and implemented computer vision algorithms for inspection and production optimization.

• Researched and deployed predictive models, guiding operational and business decisions.

• Designed PoCs and prototypes to validate new technologies in manufacturing processes.

• Built and maintained data pipelines using Dagster, ensuring robust orchestration and integration of workflows.

• Evaluation and report of the final defects of the production line. Preparation, correction, and translation of work instructions for operators.

PROJECTS

Resource allocation in wireless networks through Deep Reinforcement Learning

• Accelerated the learning of the conventional Deep Q-Network model for power allocation in wireless networks by up to 77% and improved the network performance by up to 24.7% by proposing different training strategies with transfer learning. Simulations were performed using Python with Pytorch.

• Conceptualized, analyzed, and wrote three published refereed articles in top journals from Q1 and Q2 in the computer science area.

• Conducted a systematic review methodology and identified the 56 most relevant research works implementing machine learning for resource allocation. Performed data extraction, cleaning, and visualization using Excel.

San Diego home price prediction

• Collected and scraped data using BeautifulSoup and preprocessed it by cleaning, handling missing values, and detecting outliers with Python (Pandas, Numpy, and Matplotlib).

• Trained a regression model and achieved an accuracy score of 83.7% using grid search with scikit-learn in Python.

• Deployed the trained model on a Flask server to make predictions and hosted it on a web page using HTML/CSS.

Recognition of eye diseases with neural networks

• Designed a convolution neural network with 89.2% accuracy for detecting ocular diseases using the ODIR-5K database of the Kaggle platform using Python with Keras.

• Managed and planned teamwork tasks for the preprocessing stage involving image formatting, data cleaning, and data augmentation for unbalanced classes.

Explainable AI (XAI) for beer brand classification

• Implemented GRAD-CAM, an explainable AI method, to interpret the Convolutional Neural Network (CNN) decision-making for beer brand classification.

• Fine-tuned the pre-trained VGG16 CNN architecture with additional layers to achieve an accuracy of 91.6%. Data augmentation, preprocessing, and training were performed using libraries such as Keras and Sklearn.

EDUCATION

Universidad Autónoma de Baja California – Mexicali, Baja California, México.

Universidad Autónoma de Baja California – Mexicali, Baja California, México.

Universidad Autónoma de Baja California – Mexicali, Baja California, México.

CERTIFICATIONS

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