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.
