Daksh

0+ yrs exp 76 · Very Good

About

Motivated and detail‑oriented technology graduate with strong foundations in programming, data handling, and modern frameworks. Experienced through internships and projects in building scalable applications and applying analytical problem‑solving. Dedicated to continuous learning and eager to contribute technical expertise in dynamic environments.

Skills & Expertise (28)

Python Intermediate

7.6/10

Years Exp

Time management Intermediate

7.0/10

Years Exp

Teamwork Intermediate

7.0/10

Years Exp

Communication Intermediate

7.0/10

Years Exp

React.js Intermediate

7.0/10

Years Exp

NumPy Intermediate

6.5/10

Years Exp

scikit-learn Intermediate

6.5/10

Years Exp

Pandas Intermediate

6.5/10

Years Exp

JavaScript Intermediate

6.5/10

Years Exp

Typescript Intermediate

6.5/10

Years Exp

Git Intermediate

6.0/10

Years Exp

Docker Intermediate

5.5/10

Years Exp

VS Code Streamlit C++ Php Problem Solving Prisma Postman Node.js Power BI Tableau Java Next.js MongoDb Postgresql MySql Tailwind CSS

Work Experience

Python Full Stack Developer

Infosys Springboard

Dec 2025 - Feb 2026

Developed a Fraud Rules Engine web app for insurance workflows, improving fraud detection efficiency by 25%. Engineered backend services in Python + SQL with RESTful APIs, reducing query response time by 30%. Designed modular React + TypeScript front‑end with TailwindCSS and Shadcn UI, boosting usability by 40%. Implemented audit logging, version control, and CSV export, strengthening compliance and transparency. Deployed the application on Netlify, delivering a production‑ready solution accessible to end users.

AI/ML Intern

Teachnook EdTech

Sep 2024 - Nov 2024

Gained hands‑on exposure to machine learning, deep learning, NLP, and computer vision through structured mentorship. Built micro‑projects including a sentiment analysis tool, chatbot, and data visualization dashboard, enhancing engagement by 15%. Strengthened technical skills in Python, Pandas, NumPy, Scikit‑learn, with deployment via Streamlit. Applied data preprocessing, model evaluation, and hyperparameter tuning to improve model reliability by 18%. Improved collaboration, communication, and time management through team reviews and project discussions.