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Data Science with 3+ years in Machine Learning & Analytics
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Dedicated Data Scientist and Assistant Professor with strong expertise in programming, analytics, machine learning, and computer application subjects. Experienced in developing ML models, building dashboards, conducting research, and mentoring students. Skilled in Python, SQL, Power BI, TensorFlow, PyTorch, and full-cycle data processing. Adept at designing learning materials, supervising academic projects, and supporting departmental initiatives.
Chandigarh University
MSc.Data Science · Data Science
August 1, 2022 – June 30, 2024
Kuriakose Elias College
BSc. Computer Application Triple Main · Computer Application
August 1, 2019 – June 30, 2022
Mangalam M C Varghese College of Arts & Science
Assistant Professor
August 1, 2025 – Present
India
BSNL
Apprentice
March 1, 2025 – August 1, 2025
Kottayam, Kerala, India
Soften Technologies
Jr.Data Scientist Intern
October 1, 2024 – March 1, 2025
Cochin, Kerala, India
Solitaire Infosys
Python With Data Science Intern
June 1, 2023 – July 1, 2023
Mohali, Punjab, India
HR Analytics Dashboard for Bharath Sanchar Nigam Limited (BSNL)
June 24, 2026 – Present
Built an end-to-end HR analytics dashboard analyzing employee, pension, and retirement data, reducing manual reporting by 40%. Designed DAX calculations for tenure, retirement year, age bands, and segmentation, enabling detailed workforce forecasting. Created interactive visuals covering gender, employment type, location, and departmental distribution for executive reporting. Integrated custom slicers for department, status, and geography to support multi-layer analysis. Constructed retirement trend models for long-term succession planning. Cleaned and structured raw Excel data using Power Query, improving model performance and refresh efficiency. Added drill-through pages for department-level insights to support strategic HR decisions.
View ProjectSynthetic Data Generation Using GANs
June 24, 2026 – Present
Engineered a GAN to generate synthetic handwritten digits, producing consistent outputs after 50–100 epochs. Designed CNN-based Generator and Discriminator architectures, decreasing generator loss by 60%. Applied GradientTape for customized training loops, enhancing model stability and reducing discriminator fluctuations by 25%. Generated synthetic samples every 10 epochs to monitor quality progression. Achieved >95% discriminator accuracy and improved digit clarity across epochs. Enabled model weight saving/loading to streamline retraining and experimentation by 40%.
View ProjectObject Detection with YOLOv5
June 24, 2026 – Present
Built a real-time YOLOv5s object detection app with an inference time of ~6.4 ms per image. Developed a custom-styled Gradio interface for improved usability and user interaction. Executed an annotated detection pipeline that overlays bounding boxes and labels directly on input images. Enhanced output rendering and added features such as automatic result visualization and downloadable inference images. Launched the application with Gradio share links, removing local setup requirements for users. Streamlined UI layout using CSS for a clean, professional appearance.
View ProjectCustomer Churn Prediction
June 24, 2026 – Present
Built a machine learning workflow to classify customer churn using structured telecom data. Performed data cleaning, feature encoding, and missing value imputation, improving dataset completeness to 100%. Conducted train-test segmentation (80/20) and engineered feature-target matrices for classification models. Evaluated multiple algorithms (Decision Tree, Naive Bayes, KNN, Random Forest, SVM). Identified Random Forest (~87% accuracy) and SVM (~85% accuracy) as top-performing models. Compared model performance using confusion matrices and accuracy metrics to determine optimal selection.
View ProjectCultural Fit Analysis
The candidate's background as an Assistant Professor, coupled with multiple internships and personal projects, suggests a strong drive for continuous learning and skill development. Their involvement in diverse projects like HR analytics, object detection, and synthetic data generation indicates a broad interest in applying data science across different domains. The mentoring and teaching experience aligns well with a collaborative team environment where knowledge sharing is valued. The candidate appears to be a good fit for a role that encourages innovation and practical application of data science principles.
Soft Skills & Operational Fit
The candidate demonstrates strong communication skills through their teaching experience and clear project descriptions. Their involvement in curriculum development and student mentoring suggests good organizational and leadership potential. The project diversity indicates adaptability and a proactive approach to learning new technologies. However, the lack of specific psychometric test results makes it difficult to fully assess stress handling and team collaboration under pressure.