Entry-level Software Engineer with Data Science & Machine Learning skills
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Assessing your cultural and operational fit
Software Engineer (Fresher) and a recent graduate with foundational knowledge in data science and machine learning. Gained practical experience through an internship at Ybi Foundation, developing a personalized movie recommendation system that significantly boosted user engagement and subscriptions. Proven ability to build high-accuracy deep learning models for real-world problems like potato leaf disease classification, coupled with strong competitive programming achievements.
Jain university/Bangloore
Bachelor of computer science in data science · Computer Science in Data Science
August 1, 2021 – June 30, 2025
Narayana Junior college
Senior Secondary
June 1, 2019 – May 31, 2021
Narayana International school
Higher Secondary
June 1, 2018 – May 31, 2019
Ybi Foundation
Internship-Project Management
October 1, 2025 – Present
Bengaluru, Karnataka, India
POTATO LEAF DISEASE DEEP LEARNING
June 1, 2026 – Present
diseases using CNN, MobileNetv2, Inception, and Transfer learning in potato leaves using machine learning and deep learning techniques. The model analyzes leaf images and classifies them as healthy or diseased. Developed a high-accuracy (98% Test Accuracy) system to classify potato leaf disease (Early Blight, Late Blight, Healthy) potato leaf samples, implementing CNN architectures (VGG16, ResNet-9) via TensorFlow/Keras, utilizing PlantVillage dataset augmentation, and optimizing with transfer learning.
DATA STRUCTURES AND ALGORITHMS In JAVA
Unknown
June 1, 2026 – Present
JAVA in Great learning
Great learning
June 1, 2026 – Present
python in great learning
Great learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate shows a strong interest in competitive programming, software development, and hackathons, which aligns with a culture that values continuous learning, problem-solving, and innovation. The academic project and internship demonstrate an ability to work on diverse technical challenges. However, the overall experience is limited to academic and internship settings, and there is no explicit mention of open-source contributions or team-based project methodologies beyond leading technical events, which limits the depth of cultural fit assessment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to apply technical skills to solve problems and achieve measurable results (e.g., 40% increase in user engagement). Participation in coding contests and leading technical events suggests initiative and a collaborative spirit. However, without specific psychometric or English test scores, a comprehensive assessment of communication clarity, logical reasoning, work attitude, stress handling, and team collaboration is not possible.