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Jack Bland

jack.bland2k@gmail.com · New Jersey · 732-832-1337 · GitHub · jackbland.com

Summary

MS Data Science candidate at NJIT (May 2026) with a Mathematics foundation and hands-on experience building ML models, optimizing SQL pipelines, and translating complex data into business decisions. Seeking full-time roles in data science, data analytics, or business intelligence.

Technical Skills

  • Languages: Python (Pandas, NumPy, LightGBM, Matplotlib, Seaborn), SQL (MySQL), R, JavaScript
  • Analytics & BI: Tableau, Excel, Statistical Modeling, Predictive Analytics, Exploratory Data Analysis
  • Cloud & Engineering: AWS (S3, EC2, Kinesis, Athena), Data Engineering, Query Optimization, Data Modeling
  • Tools: MySQL Workbench, Workiva, Django, Git, Oracle Data Modeler, Jupyter, RStudio

Education

  • NJIT — M.S. Data Science (Computational Track), GPA: 3.37 (Expected May 2026)
  • CUNY College of Staten Island — B.S. Mathematics, GPA: 3.3 (Dec 2023)
  • NJIT — Graduate Certificate in Data Mining
  • Promineo Tech — Data Engineering Bootcamp, AWS/SQL/Python (Aug 2024)

Experience

Internal Audit Intern — OceanFirst Bank, Red Bank NJ (Jun–Aug 2023)

  • Analyzed audit findings in Workiva to support SOX compliance; validated financial data accuracy against regulatory standards across multiple product lines.
  • Compiled structured reports and presented key findings to cross-functional stakeholders, maintaining 100% data traceability across departments.

Engineering Intern — Port Authority of NY & NJ (Summer 2019)

  • Produced data-driven workforce and environmental risk reports to support operational planning between engineering and planning teams.

Projects

  • Heart Attack Prediction & Explainable AI — 4 ensemble ML models (LightGBM best at 98.11% accuracy); LIME applied to identify key biomarkers (Troponin, KCM).
  • Clustering Algorithm Engineering — K-Means and HAC from scratch in NumPy; benchmarked across 3 trials via custom Silhouette Score (K-Means scores: 0.2559, 0.2522, 0.2535).
  • Apriori Association Rule Mining — Apriori vs brute-force from scratch in Python/MySQL; Apriori reduced runtime ~33% (0.103s vs 0.155s).
  • F1 Driver Performance Analysis — K-Means clustering and logistic regression on 6 merged F1 datasets; 59% accuracy and 73% recall predicting teammate outcomes.
  • HR Attrition Dashboard — 6-sheet Tableau dashboard on IBM HR data (1,470 records); surfaced overtime as key attrition driver.
  • Full-Stack E-Commerce App — Django app with JSON product catalog, real-time cart, user authentication, and checkout flow.
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