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Data Science with 1+ years in Machine Learning & Deep Learning.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Science professional with a background in Mechanical Engineering, possessing 1.3 years of experience in data analysis, machine learning, and deep learning. Skilled in Python, SQL, and various data science libraries, with proven ability to solve real-world problems and enhance prediction accuracy through advanced model implementation.
Luminar Technolab, Kochi, Kerala
Certified Course · Big Data Analytics & Data Science
August 1, 2022 – June 30, 2023
Kerala Technological University
B.E. · Mechanical Engineering
August 1, 2018 – June 30, 2022
Ayotta Infotech
Support Engineer
December 1, 2024 – Present
Bengaluru, Karnataka, India
Skolar
Business Development Trainee
August 1, 2024 – November 1, 2024
Bengaluru, Karnataka, India
Luminar Technohub
Data Science Intern
October 1, 2022 – June 1, 2023
Cochin, Kerala, India
Pimple Detection Tool
September 1, 2023 – September 1, 2023
Utilized Python, Keras, TensorFlow, OpenCV, and Scikit-learn to create a robust image detection system, enhancing accuracy and performance in visual data analysis. Engineered and deployed a state-of-the-art pimple detection tool using Convolutional Neural Networks (CNN), achieving 82% accuracy. Applied advanced image processing for precise analysis. Improved skincare analysis accuracy to 90% by implementing detailed data augmentation and achieved a notable 84% accuracy through refining parameters for synthetic data with the Random Forest Classifier.
View ProjectSQL and Relational Databases
IBM
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's background includes a mix of technical support, business development, and data science internships, indicating adaptability and a willingness to explore different areas. The academic project and internship in data science align with the target role. The mechanical engineering background, while not directly related, suggests a structured problem-solving approach. The current support engineer role, while not data science, involves technical troubleshooting and SQL, showing a foundational technical aptitude.
Soft Skills & Operational Fit
The candidate has experience in communicating technical findings to non-technical audiences, which is crucial for data science roles. Their support engineer role indicates problem-solving and troubleshooting skills, which are transferable. However, the primary experience is not directly in a data science operational role, which might require some ramp-up.