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Data Science with less than a year in AWS Data Engineering and AI/ML.
Actively pursuing a Bachelor of Technology in Computer Science and Engineering with a specialization in Data Science, graduating in 2026. Possesses a strong foundation in programming languages like Python, C, Java, and R, coupled with expertise in database management (MySQL) and various data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow, XGBoost). Gained hands-on experience through virtual internships in AWS Data Engineering and AI Development, building data pipelines, deploying AI/ML models, and performing data preprocessing and feature engineering. Proficient in cloud and data engineering tools such as AWS S3, Glue, and Lambda, and skilled in machine learning techniques including model training and ensemble methods. Demonstrates a keen ability to apply theoretical knowledge to practical projects like credit card fraud detection and disease prediction, achieving high performance metrics.
Gandhi Institute of Technology and Management (GITAM)
Bachelor of Technology · Computer Science and Engineering - Data Science
August 1, 2022 – June 30, 2026
Eduskills
AWS Data Engineering Virtual Internship
July 1, 2025 – June 1, 2026
India
Code Beat
Artificial Intelligence Developer Intern
July 1, 2025 – June 1, 2026
India
GCGC ACS
Gitam Data Management Intern
July 1, 2025 – June 1, 2026
India
Hybrid Deep Learning Ensemble Model for Credit Card Fraud Detection
June 1, 2025 – June 1, 2026
Built a hybrid ensemble model using CNN, LSTM, Transformer, and XGBoost for fraud detection. Applied feature engineering, preprocessing, and stratified K-fold cross-validation to handle class imbalance. Implemented stacking ensemble to enhance model robustness and generalization. Achieved strong performance with ROC-AUC 0.99994 and F1-Score 0.99930 on test data.
Disease Prediction & Prevention Model
June 1, 2025 – June 1, 2026
Built a disease prediction system using Random Forest and Gradient Boosting models. Performed data preprocessing including one-hot encoding and feature engineering. Achieved 89% accuracy using ensemble learning techniques. Provided disease prediction with prevention and treatment recommendations.
Data Engineering by AWS Academy
AWS Academy
June 1, 2026 – Present
Cloud Foundations by AWS Academy
AWS Academy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Computer Science and Engineering - Data Science, coupled with internships in AI/ML development and AWS data engineering, aligns well with a Data Science target role. The diversity of projects (disease prediction, fraud detection) and exposure to various ML/DL models and AWS tools indicate a broad interest and adaptability. The certifications from AWS Academy further strengthen the cultural fit for a data-driven, cloud-oriented environment. However, the experience level is still very junior (currently pursuing a bachelor's degree), which might impact immediate senior-level cultural integration.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to work with complex datasets. The internship experiences suggest an operational fit for roles requiring data management and AI/ML model development. However, without specific psychometric or English test scores, a comprehensive assessment of soft skills like logical reasoning, work attitude, stress handling, and team collaboration is not possible.