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Data Science with less than a year in Data Analysis & Machine Learning.
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Assessing your cultural and operational fit
Aspiring Data Analyst with hands-on experience in Python, SQL, and machine learning, supported by internship experience and academic projects. Skilled in data analysis, exploratory data analysis (EDA), and data visualization, with practical exposure to healthcare analytics and time-series data. Experienced in building machine learning models and deriving actionable insights to support data-driven decision-making.
APJ Abdul Kalam Technological University
M.Tech · Data Science
August 1, 2024 – June 30, 2026
APJ Abdul Kalam Technological University
B.Tech · Electrical and Electronics Engineering
August 1, 2018 – June 30, 2022
Board of Higher Secondary Examination, Kerala
Higher Secondary Education
June 1, 2016 – May 31, 2018
Hornbill Labs Private Limited
Data Science Engineer (Intern)
March 1, 2026 – June 1, 2026
India
Nectar IT Technologies Private Limited
Data Analyst Intern
October 1, 2025 – January 1, 2026
India
Chiller and Freezer Performance Analysis
October 1, 2025 – January 1, 2026
Developed a time-series anomaly detection system for chiller and freezer units to identify temperature spikes and abnormal behavior patterns. Applied data analysis and visualization techniques to interpret trends and support data-driven decision-making.
Ensemble Models for Skin Disease Detection
June 1, 2025 – June 1, 2026
Developed a multimodal ensemble model combining image-based CNN architectures (MobileNetV2, EfficientNetB0, Custom CNN) with metadata-based classifiers to improve skin disease classification. Implemented stacked ensembling techniques to enhance model performance and achieve improved classification accuracy 81.5%. Performed data preprocessing, feature engineering, and model evaluation using Python, TensorFlow/Keras, Scikit-learn, NumPy, and Pandas. Optimized model performance through hyperparameter tuning and validation strategies on the PAD-UFES-20 dataset.
A Dual-Pipeline NLP Framework for Long COVID Analysis and Clinical Simplification
September 1, 2024 – June 1, 2026
Analyzed 700+ Long COVID patient narratives and 5,000+ clinical notes to extract actionable healthcare insights from unstructured text data. Performed data preprocessing, extraction, topic modeling and symptom trend analysis to identify recurring Long COVID patterns. Developed analytical workflows for severity, duration, trajectory, and symptom co-occurrence analysis, transforming raw text into structured datasets. Generated patient-friendly summaries and visualized key findings using data-driven reporting techniques. Achieved 0.88 F1-score in extraction, 0.62 topic coherence score, and 0.89 BERTScore-F1 during model evaluation.
Data Analytics with Python
NPTEL
June 1, 2026 – Present
SQL for Data Science
Coursera
June 1, 2026 – Present
Introduction to AI
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from healthcare NLP to skin disease detection and industrial anomaly detection, indicates a broad interest in applying data science across different domains. Their academic background in Electrical and Electronics Engineering followed by a Master's in Data Science shows a strong commitment to the field. The internship experiences, though short, suggest an ability to integrate into professional environments. The candidate's skills align well with a Data Science role, demonstrating a willingness to learn and adapt to new technologies and challenges.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning and applying data science concepts through academic projects and internships. Their project descriptions indicate an ability to work on complex problems and deliver actionable insights. The automation of Excel reports and development of a dummy API suggest an operational mindset focused on efficiency and robustness. However, the lack of completed technical tests makes it difficult to fully assess problem-solving under pressure or collaboration skills.