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Data Science with less than a year in Machine Learning & AI
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Data Scientist and Machine Learning Engineer with hands-on experience building end-to-end ML/Al pipelines in TensorFlow, Scikit-learn, and Python. Delivered a computer vision object detection model at 92% accuracy and improved NLP classification accuracy by 15% through systematic hyperparameter tuning. Skilled in deep learning (CNNs), natural language processing (NLP), retrieval-augmented generation (RAG), large language models (LLMs), and generative Al. Proficient in data preprocessing, exploratory data analysis (EDA), feature engineering, SQL, Power BI, and cloud-ready model deployment. Seeking a full-time Data Scientist or Machine Learning Engineer role to build scalable Al solutions.
Dhanalakshmi Srinivasan University
Bachelor of Technology (B.Tech) · Artificial Intelligence & Data Science
August 1, 2021 – June 30, 2025
One Stop.ai
Data Science Intern
August 1, 2024 – September 1, 2024
India
Intern Certify
Artificial Intelligence Intern
January 1, 2024 – December 31, 2024
India
Alzheimer's Disease Prediction
January 1, 2024 – December 31, 2024
Built a machine learning classification model on healthcare data to predict Alzheimer's disease, achieving 90%+ accuracy using Scikit-learn. Performed comprehensive EDA to identify key health patterns, feature correlations, and risk factors in patient datasets. Engineered features via missing-value imputation, feature scaling, and selection; evaluated models using Accuracy, Precision, Recall, and F1-score. Tuned hyperparameters systematically using GridSearchCV, reducing false-negative rate — critical for medical classification tasks.
SMS Spam Detection
January 1, 2024 – December 31, 2024
Designed an end-to-end NLP pipeline — tokenization, stopword removal, lemmatization, and TF-IDF vectorization — for binary text classification. Trained a Logistic Regression model on 5,000+ preprocessed SMS messages, achieving high precision and recall scores for spam detection. Validated model robustness using k-fold cross-validation and confusion matrix analysis, minimizing false positives. Packaged the pipeline for reuse, enabling fast inference on new unseen text inputs with minimal preprocessing overhead.
Visual Target Identification
January 1, 2024 – December 31, 2024
Architected a Convolutional Neural Network (CNN) using TensorFlow and Keras for multi-class image classification and visual target identification. Applied data augmentation (rotation, zoom, horizontal flip) and preprocessing to expand training diversity and reduce overfitting. Improved classification accuracy through dropout regularization, batch normalization, and hyperparameter tuning across 20+ training runs. Deployed model pipeline for real-time image prediction, integrating preprocessing and inference into a single end-to-end flow.
Data Science Internship Certificate
One Stop.ai
June 1, 2026 – Present
Data Analysis with Python
IBM
June 1, 2026 – Present
SQL and RDBMS
IBM
June 1, 2026 – Present
Data Science Program
EXCELR
February 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio (healthcare, NLP, computer vision) and interest in cutting-edge areas like Generative AI and LLMs indicate a curious and adaptable mindset. Their participation in Kaggle Competitions suggests a drive for continuous improvement and problem-solving. The stated availability for multiple roles (Data Scientist, Data Analyst, Machine Learning Engineer) shows flexibility, but also a potential lack of focused career direction at this early stage. The candidate is currently pursuing a Bachelor's degree, which means they are still in an academic setting, and their professional experience is limited to internships. This might require a team that is supportive of early-career professionals.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning and applying advanced ML/AI techniques. Their project descriptions highlight attention to detail in model validation and optimization. Collaboration is mentioned in the internship experience, indicating an ability to work in a team. The candidate's interest in Generative AI, LLMs, and RAG suggests a desire to stay current with emerging technologies, which is a positive for operational fit in an innovative environment.