Aminul Islam Shaik
Data Scientist & AI Project Lead specializing in machine learning, cloud computing, and business intelligence. Transforming complex data into actionable insights that drive operational excellence and strategic decision-making.
LinkedIn Profile
Technical Expertise
Programming
Python, Java, C++, C#, SQL, R with expertise in Pandas, NumPy, Scikit-learn for data manipulation and analysis.
Cloud & Big Data
Google Cloud Platform, AWS, Hadoop, Spark, DynamoDB, neo4j, Microsoft Fabric for scalable solutions.
Machine Learning
Deep Learning, NLP, Time Series Forecasting, Ensemble Models, CNN, RNN, LSTM using TensorFlow, Keras, PyTorch.
Analytics & BI
Tableau, Power BI, Matplotlib, Seaborn, Plotly for creating compelling data stories and executive dashboards.
Education & Research
Master of Science in Data Science
University of Nebraska at Omaha
IT Concentration | Expected Dec 2025
Focus Areas: Data Visualization, Machine Learning, Cloud Computing, Statistical Analysis
Research: Mobility patterns, Electric Vehicle Data Analysis, Cloud Computing applications
Achievement: Developed multi-criteria decision-making models resulting in published IEEE paper on data-driven decision frameworks.
AI Project Lead at Hustad Companies
June 2025 – Present | Omaha, NE
20-30%
Faster Processing
Inspection to sale cycle time reduction
5+
AI Agents
Deployed across operations
3+
Dashboards
Production-ready analytics
Leading company-wide AI and automation initiatives across Sales, Service, and Inspections. Deployed n8n + GPT + CenterPoint agents for ticket validation, quoting, reporting, and email triage. Applied ML classification, forecasting, and anomaly detection to accelerate decisions. Built Excel and Power BI dashboards for KPIs, backlog visibility, and demand forecasting.
Professional Experience Journey
1
Data Analyst - UNO Auxiliary Services
Feb 2025 – May 2025: Processed multi-departmental data for dining, childcare, parking, and bookstore operations. Generated executive dashboards delivering actionable insights for planning decisions.
2
ML & Data Mining - UNO
Feb 2024 – Dec 2024: Conducted data mining on millions of records using SQL, Python, and R. Developed automated reports and dashboards, acting as junior data consultant for business teams.
3
Associate Software Developer - CGI
Jan 2022 – Dec 2023: Developed ASP.NET Core applications using C# and .NET 6.0. Built backend services with Entity Framework Core and SQL Server, improving workflow automation.
Featured Projects
Daycare Staff Forecasting
Led ML modeling using PyGAM to forecast staffing for Child Saving Institute. Cleaned multi-year time-series data, engineered 30-min interval features, generated weekly predictions supporting operational planning and cost efficiency.
BLS Insights Analysis
Developed quantitative risk model using Python and ML libraries. Built interactive dashboard visualizing risk factors. Automated data collection from public sources ensuring real-time updates for strategic decision-making.
Sales Insights Dashboard
Designed dynamic Tableau dashboard improving data accessibility. Automated updates using Power BI Gateways, reducing manual reporting by 40%. Conducted EDA identifying key sales trends and optimizing revenue strategies.
Competition Success
1
FNBO Datathon - 2nd Place
November 2024: Analyzed reward programs identifying customer engagement patterns. Developed ML model for customer segmentation. Designed Power BI dashboards visualizing trends, presenting actionable recommendations to industry experts.
2
UNO Data Mining Contest #2 - 3rd Place
Spring 2025: Built predictive ML model using ensemble techniques among 15 competing teams. Extracted patterns from real-world datasets demonstrating advanced data mining capabilities.
3
UNO Data Mining Contest #1 - 2nd Place
Spring 2025: Applied advanced model tuning methods among 6 teams. Developed actionable insights showcasing expertise in competitive data science environments.
Environmental Impact Project
GHG Emissions Analysis by State
Durham Science, Omaha | Feb 2024 – Aug 2024
Created web application with Plotly analyzing U.S. emissions data, increasing processing efficiency by 30% to support policymaking. Integrated external climate datasets for comprehensive trend analysis. Conducted time-series forecasting of future greenhouse gas emissions trends.
Published Research
IEEE Publication
Authored and published research paper on multi-criteria decision-making models, demonstrating expertise in data-driven frameworks for complex business problems. The work contributes to academic understanding of quantitative decision analysis and practical applications in organizational contexts.
Let's Connect
Location
Omaha, NE 68106
Phone
+1 (402) 213-2762
Passionate about leveraging data science and AI to solve complex business challenges. Open to opportunities in machine learning, data analytics, and AI-driven automation. Let's discuss how data-driven insights can transform your organization.
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