Ms. NEHA JHAKRA

Ms. Neha Jhakra is a Teaching Assistant in the School of Engineering and Technology with a background in Computer Science and Data Analytics. She holds a Master’s degree in Computer Science from Banasthali Vidyapith. Her professional experience includes working as a Data Analyst at AU Small Finance Bank, where she developed SQL-based data pipelines, automated reporting processes, and built Power BI dashboards for financial and risk analysis. She has also worked as a Data Science and Quantum Research intern at MeitY-NIC, where she focused on quantum-enhanced algorithms and data analytics. Her academic interests include Generative AI, Natural Language Processing (NLP), and data-driven analytics. She enjoys teaching and mentoring students in AI and analytics-related topics and guiding them to apply analytical and AI techniques to solve real-world problems through project-based learning.

• M.Sc. in Computer Science – Banasthali Vidyapith, Jaipur (CGPA: 8.2)
• B.Sc. in Computer Science – Maharishi Dayanand University, Gurugram

Data Analyst – AU Small Finance Bank, Jaipur
Oct 2023 – Present

• • Developed SQL queries and automated reporting workflows to improve operational efficiency.
  • Designed interactive Power BI dashboards for monitoring financial and fraud risk KPIs.
  • Performed data analysis on financial and transactional datasets to identify trends and anomalies.
  • Collaborated with audit and compliance teams to maintain high data quality standards.

Data Science & Quantum R&D Intern – MeitY-NIC, New Delhi
Dec 2022 – Jun 2023

• • Worked on quantum-enhanced algorithms for image edge detection.
  • Implemented Python-based analytical workflows using Matplotlib and Plotly.
  • Conducted research on hybrid quantum-classical computing models.

• Credit Card Fraud Detection System – Built an end-to-end fraud detection pipeline using SQL, Python, and Machine Learning models with Power BI dashboards.
• Amazon Review Topic Modelling (NLP Project) – Applied topic modelling techniques such as LDA and NMF to analyze product reviews and extract insights.
• Telecom Customer Churn Analysis – Developed churn prediction models using SQL and Python with interactive analytics dashboards.