I am a data-driven professional specializing in IT operations analytics, predictive modeling, and interactive data visualization. With a background in Industrial Engineering and Data Science, I focus on transforming operational data into actionable insights that improve service performance, forecasting accuracy, and decision-making.

Currently working in IT Metrics and Analytics, where I design predictive models and dashboards that support infrastructure capacity planning, service desk optimization, and anomaly detection.

Predictive Analytics: Developed forecasting models using Prophet and XGBoost to predict infrastructure demand and support capacity planning.

Machine Learning Applications: Built a Service Desk abandonment risk model using Logistic Regression and XGBoost achieving 75% recall for high-risk cases.

Operational Monitoring: Designed anomaly detection dashboards and heatmaps to identify unusual operational patterns.

Forecasting Performance: Achieved MAPE of 9% when predicting DevOps ticket demand using Holt-Winters models in R.

Softtek | 2025 – Present

• Built predictive models using Prophet and XGBoost to forecast database connection demand.
• Developed a Service Desk abandonment risk model to detect high-risk customer cases.
• Created heatmap dashboards and anomaly detection tools for operational monitoring.

Softtek | 2019 – 2024

• Developed forecasting models (ARIMA, Prophet, Holt-Winters) for DevOps ticket demand.
• Built Tableau dashboards to analyze customer satisfaction trends.
• Supported ISO/IEC 20000-1:2018 internal and external audits.

Softtek | 2015 – 2019

• Conducted compliance assessments across international IT service teams.
• Developed Power BI dashboards to streamline audit reporting.
• Maintained zero audit findings for two audit cycles on a strategic enterprise account.

Alongside my professional work, I build personal data science projects to explore statistical modeling, machine learning, and interactive analytics.

Process Capability Analyzer (Shiny App)

An interactive R/Shiny application that allows engineers and analysts to upload process data, visualize distributions, and calculate process capability indices (Cp, Cpk) used in quality control and manufacturing analysis.

• Repository

CheemsPlot – Interactive Dog-Styled Boxplot Explorer

A playful R/Shiny visualization tool that replaces traditional boxplot outliers with customizable Cheems icons, demonstrating how statistical visualization can be both analytical and engaging.

• Shiny App
• Repository

• MSc in Data Science and Information INFOTEC – Mexico (In progress) Badge

• BSc in Industrial Engineering Tecnológico Nacional de México

• Kaggle – Intro to SQL
• Kaggle – Intro to Machine Learning
• Kaggle – Intermediate Machine Learning
• Kaggle – Pandas
• Kaggle – Data Visualization
• Kaggle – Time Series
• Kaggle – Data Cleaning
• Kaggle – Feature Engineering
• Udemy - Mathematics and Statistics for Machine Learning
• Simplilearn - Data Science with Python
• Simplilearn - Data Science with R
• Softtek - Interpretation of the Standard & Internal Auditor for ISO/IEC 20000
• Axelos - ITIL v3 Foundation Certification

📍 Location: Aguascalientes, Mexico

📧 Email: sullivan.santana@gmail.com

🔗 LinkedIn: sullivan santana

My interests—music, outdoor sports, and technology—reflect a balance of creativity, discipline, and analytical thinking. I currently work in the information technology field, where I have developed strong skills in process compliance and data analytics. This experience has reinforced the importance of innovation, continuous improvement, and collaboration in a fast-paced business environment.

As a dedicated professional with strong teamwork and problem-solving abilities, I bring the same focus and curiosity I apply when exploring new musical genres or riding challenging mountain biking trails to my professional work and personal projects.