Anuj G.

About Anuj G.

I am a Data Analyst with 3+ years of experience in building analytics solutions, machine learning models, and reporting systems that support large-scale federal data operations. I currently work at SAG Corporation supporting the U.S. Department of Veterans Affairs, where I develop data pipelines, predictive models, and business intelligence dashboards that enable leadership to make data-driven decisions.My work spans the full analytics workflow, including data extraction, transformation, modeling, and visualization. I maintain SQL-based data pipelines that power operational reporting systems and executive dashboards used by VA leadership. I also contribute to the Navy Enlisted Management Model (NEMMo) project, where I develop and evaluate machine learning models—including XGBoost and decision trees—to forecast duty status outcomes for Navy personnel using large-scale historical datasets.In addition to technical analytics work, I collaborate closely with stakeholders across federal programs to translate operational requirements into data solutions. I have supported reporting initiatives, built Power BI dashboards for performance monitoring, and implemented automated workflows to improve reporting accuracy and efficiency.Previously, I worked at the Federal Reserve Bank of Richmond where I used SQL and Tableau to analyze certification and compliance data, build interactive dashboards, and automate reporting processes. Earlier in my career, I provided IT support in a secure government contracting environment, where I gained experience supporting enterprise systems and received my Active Secret Security Clearance.My technical interests include machine learning, predictive modeling, data engineering, and business intelligence. I enjoy building scalable analytics solutions that transform complex data into actionable insights!

Experience

1. Data Analyst

   Sag Corporation

   Dec 2023 — Present · Alexandria, VA, US

   Maintained and optimized 50+ SQL pipelines supporting VA claims processing and executive reporting systems.• Developed machine learning models (XGBoost, decision trees) to forecast Navy enlisted duty status using 100K+ historical records.• Built data preprocessing pipelines to clean and transform structured personnel datasets for model training.• Engineered predictive features to improve classification performance and model accuracy.• Tuned model hyperparameters using Bayesian optimization and cross-validation.• Conducted model experimentation across gradient boosting algorithms to evaluate predictive robustness.• Evaluated model performance using confusion matrices, feature importance analysis, and classification metrics.• Designed and deployed Power BI dashboards to monitor claims KPIs and operational performance.• Implemented data validation checks and automated reporting workflows to improve reporting accuracy.

Education

• Virginia Commonwealth University

  Bachelor of Science - BS, Computer Science