Open to opportunities

Nida Nishmath User

@nidanishmathuser

Message

I am a Data Scientist specializing in ML and model deployment

India

Message

What I'm looking for

I seek an entry-level ML/Data Science role where I can apply deep learning and deployment skills, work on real‑time systems, collaborate in cross‑functional teams, and grow technical responsibility while delivering measurable business impact.

I am an MSc Data Science candidate at Vellore Institute of Technology with a strong academic record (CGPA 9.75) and a background in mathematics (BSc, CGPA 9.23). I focus on practical, high-impact machine learning solutions that move from prototype to production.

In internships I built an interactive dashboard from web‑scraped car data that cut report generation time by 40%, developed a CNN‑LSTM image captioning pipeline with 92% accuracy, and led a real‑time face‑mask detection system achieving ~96% training and 94% test accuracy. I improve model robustness through hyperparameter tuning, preprocessing, and clear visualizations.

On projects I engineered a hybrid Transformer‑LSTM stock predictor (R² 0.9781) that substantially lowered MSE/RMSE/MAE versus baselines and validated performance across markets. I enjoy end‑to‑end work—EDA, modeling, deployment—and I seek roles where I can apply deep learning, ML engineering, and data visualization to deliver measurable business impact.

Experience

Work history, roles, and key accomplishments

Current

Data Scientist Intern

Current

Evoastraventures

Jul 2025 - Present (1 month)

Developed an interactive data-visualization dashboard from web-scraped car datasets, reducing report generation time by 40% and improving stakeholder data access. Designed and fine-tuned a CNN-LSTM image-captioning model achieving 92% accuracy and a 15% improvement in caption relevance, enabling real-time deployment.

Python Web Scraping Data Visualization CNN LSTM Hyperparameter Tuning TensorFlow

Current

AI & ML Intern

Current

Elevate Labs

Jul 2025 - Present (1 month)

Implemented foundational machine learning algorithms and ensemble techniques that improved predictive accuracy by 10% and used dynamic visualizations to optimize model performance by 15%. Led design and deployment of a real-time face-mask detection system achieving ~96% training accuracy and 94% testing accuracy.