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This page provides a detailed overview of my professional experience, education, and skills related to information systems and cybersecurity.

Basics

Name Shane Reichlin
Label Data Enthusiast
Url https://shanereichlin.com
Summary Master's Student at the Johns Hopkins Whiting School of Engineering with a background in cybersecurity and business analysis.

Work

  • 2023.09 - Present
    Business Analyst
    WES Construction
    Optimizing business operations through data analysis, financial reporting, and project cost evaluation. Focused on process improvement, stakeholder collaboration, and strategic decision-making to drive efficiency and profitability.
  • 2022.08 - 2023.09
    Cyber and Strategic Risk Analyst
    Deloitte
    Enhancing client security solutions through data analytics, PAM (Privileged Access Management) administration, and Python automation.
  • 2021.06 - 2021.09
    Summer Analyst
    Aghaz Investments
    Refactoring financial analysis scripts, simulating historical returns for generated portfolios, and assessing investment vehicles.

Education

  • 2024.01 - Present

    Baltimore, Maryland

    Master's of Science
    Johns Hopkins Whiting School of Engineering
    Information Systems Engineering
    • Software Engineering
    • Information Systems Engineering
  • 2018.09 - 2022.05

    Seattle, Washington

    Bachelor's
    University of Washington Foster School of Business
    Information Systems and Finance
    • Data Analytics
    • Business Intelligence
    • Machine Learning
    • Predictive Analytics
    • Statistical Modeling
    • Database Management
    • Cloud Computing

Volunteer

Certificates

Skills

Data Analytics
Power BI
Tableau
Looker
Excel
Programming & Scripting
Python
SQL
R
Java
Machine Learning
Numpy
Pandas
Scikit-learn
TensorFlow
Matplotlib
Soft Skills
Critical Thinking
Time Management
Adaptability
Collaboration

Projects

  • 2024.06 - 2024.06
    Consumer Segmentation Analysis
    Conducted an in-depth customer segmentation analysis using clustering techniques to identify distinct customer groups based on purchasing behaviors and demographics. Developed detailed profiles for each segment to inform targeted marketing strategies and enhance customer engagement.
    • Python
    • Machine Learning
    • K-Means Clustering
    • Marketing Analytics
    • Data Visualization
  • 2024.04 - 2024.05
    Credit Card Fraud Detection
    Developed a robust fraud detection model using XGBoost, which achieved high precision and recall in identifying fraudulent transactions. Leveraged advanced machine learning techniques and data preprocessing to ensure efficient and accurate fraud detection.
    • Python
    • Machine Learning
    • Scikit-learn
    • XGBoost
    • Model Tuning