Data Extraction and Analytical Pipeline Development 

• Design and implement scalable data collection and structuring workflows using REST APIs, Apache Spark, and Microsoft Azure Data Factory. 

• Acquire and integrate multi-source datasets containing millions of records to support advanced analytics and machine learning applications. 

• Develop reproducible analytical data preparation workflows and predictive analytics solutions. 

Data Preparation and Feature Engineering 

• Perform advanced data wrangling, cleansing, normalization, aggregation, and transformation across heterogeneous datasets. 

• Apply statistical data cleaning techniques, missing value imputation, standardization, and feature engineering methodologies. 

Statistical Analysis and Mathematical Modeling 

• Develop predictive frameworks utilizing multivariate regression analysis, distribution modeling, hypothesis testing, and anomaly detection techniques. 

• Design models to evaluate relationships among high-dimensional variables and support forecasting and operational decision-making. 

Machine Learning Model Development and Evaluation 

• Design, train, validate, and deploy predictive models using linear regression, logistic regression, gradient boosting, and classification algorithms. 

• Perform model optimization, hyperparameter tuning, and performance validation using metrics such as accuracy, precision, and recall. 

Data Quality Frameworks and Experimental Validation 

• Develop automated validation procedures to detect inconsistencies, duplicates, and computational errors. 

• Implement rule-based and statistical validation methodologies to ensure analytical accuracy, reproducibility, and reliability of model outputs. 

Data Visualization and Analytical Reporting 

• Create dashboards and analytical visualizations using Microsoft Power BI and Python. 

• Communicate statistical findings, predictive model outputs, and key performance indicators to business stakeholders. 

Stakeholder Consultation and Technical Documentation 

• Collaborate with business leaders and cross-functional stakeholders regarding analytical methodologies and model interpretation. 

• Prepare technical documentation describing methodologies, assumptions, model performance, and data transformation logic. 

• Present analytical findings and recommendations to technical and non-technical audiences. 

Right of Way (ROW) and Surplus Domain Responsibilities 

• Analyze property acquisition, easement, relocation, and land management datasets to identify trends and improve operational decision-making. 

• Develop predictive models supporting surplus property inventory management, valuation, and asset disposition activities. 

• Perform geospatial analysis and integrate GIS datasets to support Right of Way and surplus management initiatives. 

• Build analytical solutions that improve identification of surplus properties, asset utilization, and disposition forecasting. 

• Develop reporting and analytics solutions supporting transportation agencies, state and local governments, and public-sector property management organizations.