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Data Analyst with less than a year in biomedical data analysis and predictive modeling using Python,
Detail-oriented Data Analyst with an M.Sc. Bioinformatics background (CGPA: 9.11/10) and hands-on experience collecting, analysing, and interpreting large, complex real-world datasets to surface actionable insights. Proficient in Python (Pandas, NumPy, Matplotlib, Seaborn), SQL (data querying and filtering), and Excel for data analysis, reporting, and visualisation. Demonstrated ability to identify trends and patterns in high-dimensional data, translate findings into clear reports, and communicate data-driven recommendations to both technical and non-technical stakeholders. Eager to support smarter business decisions and drive growth at Polluxa as a Data Analyst.
Pondicherry University
M.Sc. Bioinformatics · Bioinformatics
August 1, 2024 – June 30, 2026
Vinoba Bhave University
B.Sc. Biotechnology · Biotechnology
August 1, 2021 – June 30, 2024
Plasmid
Data Analysis & Reporting Intern
May 1, 2025 – July 1, 2025
India
Bolt IoT
Data & AI Research Intern
December 1, 2024 – January 1, 2025
India
Multimodal Data Analysis & Predictive Insights
June 19, 2026 – Present
Collected and integrated 3 heterogeneous real-world data sources (biological, clinical, and behavioural) into a unified analysis pipeline using Python (Pandas, NumPy, Scikit-learn) - performing end-to-end EDA, data cleaning, normalisation, and trend identification across all modalities. Built predictive models achieving AUC = 0.99; applied SHAP to generate interpretable feature-importance reports that clearly identified the top drivers behind predictions - translating complex model outputs into actionable insights for non-technical stakeholders. Delivered fully documented, reproducible code on GitHub with clear reporting of methodology, findings, and recommendations - demonstrating data accuracy, attention to detail, and presentation-ready analytical output.
View ProjectLarge-Scale Dataset Analysis & Trend Discovery
June 19, 2026 – Present
Analysed a high-dimensional dataset of 1,232 records × 20,697 variables to identify statistically significant trends and patterns - surfacing 16,528 meaningful signals from noisy data using rigorous statistical methods (FDR-corrected testing, normalisation, and dimensionality reduction). Created a suite of publication-quality visualisations - PCA plots, heatmaps, and volcano plots — to communicate data-driven insights clearly; engineered a fully automated, reproducible analysis pipeline in Python and R with complete documentation, supporting team collaboration and iterative development.
View ProjectSupervised ML: Regression & Classification
Stanford/Coursera
June 1, 2026 – Present
100 Days of Python Bootcamp
Udemy
June 1, 2026 – Present
Deloitte Data Analytics Job Simulation
Forage
June 1, 2026 – Present
Next Generation Sequencing (NGS)
Coursera
June 1, 2026 – Present
The candidate scored 94% on the 'Data Scientist — Artificial Intelligence' exam, indicating a very strong grasp of the core concepts and practical applications in data science and AI, particularly in predictive modeling and data analysis.
Strengths
Cultural Fit Analysis
The candidate's academic projects and internships show a strong inclination towards data-driven problem-solving and a commitment to reproducible, well-documented work. Their experience with diverse datasets (biomedical, clinical, behavioral) and interdisciplinary teams suggests adaptability and a collaborative mindset. The certifications indicate a proactive approach to continuous learning and skill development, which aligns well with a growth-oriented culture. The target role of Data Analyst is a good fit for their demonstrated skills and interests.
Soft Skills & Operational Fit
The candidate demonstrates strong self-driven execution and fast learning capabilities, as highlighted in their key competencies. Their project descriptions emphasize clear communication, attention to detail, and the ability to work in interdisciplinary teams, which are crucial for operational fit. The psychometric test score of 373/500 suggests a reasonable work attitude and logical reasoning, though specific details on stress handling and team collaboration are not explicitly detailed beyond the score.
Limitations