I’m a data enthusiast with a strong interest in data analysis and a passion for using data to solve real-world problems. I’ve had the opportunity to apply my skills in various domains through hands-on projects and hackathon experiences, where I developed AI-based solutions like electricity demand forecasting, wafer fault detection, fashion ontology creation, and water quality index prediction.
I am particularly drawn to roles that allow me to work with large datasets, uncover trends, and translate those insights into actionable recommendations. I thrive in environments that emphasize collaboration and am eager to contribute to projects that have a measurable impact.
What sets me apart is my ability to combine technical proficiency with a keen interest in problem-solving. I am comfortable working with tools like SQL, Python, and Excel for data manipulation and analysis. I also enjoy the process of cleaning and preparing data to ensure its accuracy before conducting in-depth analysis.
I am always eager to learn and grow, especially in areas where I can apply my analytical mindset and work with diverse teams to develop data-driven solutions that improve decision-making and efficiency.
Experience
iNeuron
Aug 2024 – Feb 2025
Remote
Data Scientist
– Developed a phishing classifier with 96% accuracy using machine learning techniques, improving threat detection.
– Enhanced robustness of the detection system through optimization of key metrics.
– Technologies used: Python, Scikit-learn, Git, Matplotlib.
Unified Mentor
Jun 2024 – Jul 2024
Remote
Data Scientist
– Analyzed employee data to uncover trends, improving satisfaction by 15%.
– Streamlined data preprocessing, ensuring accuracy and resolving inconsistencies
