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Data Analyst with 1+ years in SQL, Python, and Machine Learning
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Data Analyst with combined academic and professional experience supporting data-driven decision-making, program evaluation, and reporting. Holds a Master's degree in Data Science and experienced in data extraction, transformation, aggregation, statistical analysis, data mining, and dashboard development. Strong ability to identify trends, ensure data quality, assess risks, communicate findings through analytical reports and deliver actionable insights using SQL, Python and Excel.
University at Albany, SUNY
Master of Science · Data Science
August 1, 2024 – June 30, 2026
Acharya Institute of Technology
Bachelor of Engineering · Computer Science & Engineering
August 1, 2019 – June 30, 2023
Cognizant Technology Solutions
Data Analyst Trainee
August 1, 2023 – July 1, 2024
India
Comp-Soft Technology
Web Design Intern (Data & Database Support)
May 1, 2022 – August 1, 2022
India
IBM Employee Attrition Analysis
June 23, 2026 – Present
Built and compared Logistic Regression, Random Forest, XGBoost, and SVM models for employee attrition prediction using the IBM HR Analytics dataset. Developed end-to-end ML pipelines with feature engineering, preprocessing, and hyperparameter tuning using GridSearchCV to improve prediction performance on imbalanced HR data and evaluated performance using ROC-AUC and accuracy metrics.
Fine-Grained Ship Detection in Aerial Imagery
June 23, 2026 – Present
Built an end-to-end ship detection system using YOLOv8 on the ShipRSImageNet dataset (4,500+ aerial images, 50 ship classes), achieving 63% mAP@50 on test data and matching industry benchmark performance. Performed data preprocessing, model training, hyperparameter tuning, and evaluation using PyTorch, OpenCV, and Ultralytics YOLOv8; analyzed performance using mAP, precision, recall, and IoU metrics. Conducted error analysis on dense maritime scenes, identifying small-object detection and dock-ship confusion as key challenges, with proposed improvements for multi-scale detection.
Healthcare Stroke Risk Analysis
June 23, 2026 – Present
Developed stroke risk prediction models using Logistic Regression, Random Forest, and XGBoost on 8,700+ healthcare records to identify high-risk patients. Applied MICE imputation, SMOTE balancing, and feature transformations to improve data quality and model performance on imbalanced medical datasets. Built end-to-end machine learning pipelines in R with data preprocessing, feature engineering, model evaluation, and statistical analysis for healthcare risk prediction.
Phishing Website Detection
June 23, 2026 – Present
Developed a phishing website detection system using machine learning models to classify malicious and legitimate websites based on URL and security-related features. Built a complete data pipeline including SQL-based data extraction, preprocessing, feature engineering, and model evaluation to improve phishing detection accuracy and threat identification.
100 Days of Code: The Complete Python Bootcamp
Unknown
June 1, 2026 – Present
The Data Science Course 2022: Complete Data Science Bootcamp
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects showcase a diverse range of applications, from computer vision (ship detection) to HR analytics, healthcare risk analysis, and cybersecurity (phishing detection). This breadth of interest and application, combined with experience in a large IT services company (Cognizant), suggests adaptability and a willingness to tackle varied challenges. The ongoing Master's degree indicates a commitment to continuous learning and professional development, which aligns with a growth-oriented culture.
Soft Skills & Operational Fit
The candidate demonstrates analytical thinking, problem-solving, and collaboration skills through project descriptions and professional experience. Their ability to conduct error analysis, identify inconsistencies, and work with cross-functional teams suggests a good operational fit for data-driven environments. The focus on data quality and validation aligns well with the meticulous nature required for a Data Analyst role.