Eliana Collazos
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with 1+ years in Machine Learning & Data Engineering
Eliana Collazos is a Machine Learning Engineer with professional experience in applied AI and data engineering projects. She is proficient in Python, Pandas, SQL, scikit-learn, XGBoost, LightGBM, TensorFlow, Docker, AWS, Matplotlib, and Seaborn, with hands-on experience in deep learning, supervised learning, NLP, and data pipeline development. Her background includes deploying CNN-based image classification models, building credit risk scoring models with high AUC, developing sentiment analysis pipelines, and designing end-to-end ELT pipelines. Eliana also has leadership experience, having served as President of the IEEE Women in Engineering chapter, leading technical workshops and STEM outreach initiatives for over 200 students.
Anyone AI
ML Developer
June 1, 2025 – October 31, 2025
Talento TECH - Ministry of ICT
Data Analyst
February 1, 2025 – May 31, 2025
Universidad del Cauca
Electronic and Telecommunications Engineer
August 1, 2018 – June 30, 2026
Outlier AI
AI Data Annotator
January 1, 2026 – March 31, 2026
India
Anyone AI
Machine Learning Engineer
June 1, 2025 – October 31, 2025
India
IEEE Universidad del Cauca
Consultante IT - Women in Engineering (WIE)
March 1, 2025 – March 31, 2026
India
Credit Risk Analysis
September 1, 2025 – September 30, 2025
Developed a machine learning model to predict individual credit scores using large-scale transactional datasets, improving the accuracy of credit risk assessment and enabling data-driven lending decisions. Executed extensive Exploratory Data Analysis (EDA) and implemented robust preprocessing and feature engineering pipelines in Python (Jupyter, Pandas, Scikit-learn), resulting in a cleaner and more reliable training dataset. Trained, validated, and simulated model performance on test datasets to evaluate profitability scenarios for financial institutions, demonstrating the model's potential to optimize credit approvals and minimize default-related losses. Deployed a fully Dockerized API with a simple user interface for real-time credit score predictions, facilitating easy integration into bank or fintech environments and ensuring reproducibility and scalability.
E-Commerce Data Pipeline & Business Insights
June 1, 2025 – June 30, 2025
Built a basic ELT (Extract, Load, Transform) data pipeline to analyze sales performance data from one of the largest e-commerce platforms in Latin America. Extracted structured data from CSV files using Pandas and queried APIs with Python to gather external information. Loaded and integrated datasets from multiple sources into a unified SQLite database. Transformed the data using SQL and Pandas to perform in-depth exploration and aggregation of key metrics such as daily revenue and order counts. Visualized insights through plots created with Matplotlib and Seaborn to identify business trends and performance drivers.
Professional Python Course
Código Facilito
May 1, 2025 – Present
Introduction to Power BI
Fundación Telefónica Colombia
May 1, 2025 – Present
Cultural Fit Analysis
The candidate's involvement with IEEE Women in Engineering demonstrates a commitment to diversity and inclusion, which aligns well with a positive cultural fit. Their experience in educational outreach and community engagement suggests a collaborative and supportive mindset. The project diversity, ranging from credit risk analysis to e-commerce data pipelines and image classification, indicates adaptability and a broad interest in AI applications.
Soft Skills & Operational Fit
The candidate's experience leading technical workshops and STEM outreach initiatives at IEEE suggests strong communication, leadership, and teamwork skills. Their role as an AI Data Annotator indicates attention to detail and adherence to quality standards. The project descriptions are clear and highlight problem-solving abilities and a structured approach to development.