Research Associate Position under the Professor Vasundhara Sharma Research Associate Position under the Professor Vasundhara Sharma Research Associate position under Professor Vasundhara Sharma

- Research Summary
- Roles and Responsibilities
- Required Skills and Qualifications

Role: Research Associate

Research Summary of the Faculty Professor: Vasundhara Sharma

Professor Vasundhara Sharma is an Assistant Professor of Information Systems at the Indian School of Business (ISB). She obtained her PhD in Risk and Operations Management from the McCombs School of Business, University of Texas at Austin. Her research primarily focuses on open and decentralised innovation and business models. She also works in the areas of cryptocurrencies and blockchain applications.

Prior to her doctoral studies, Professor Sharma worked as an Analyst at Goldman Sachs. She holds a B.E. in Computer Science from Birla Institute of Technology, Mesra, and completed an exchange semester at Mannheim University of Applied Sciences.

About ISB

The Indian School of Business (ISB) evolved from the need for a world-class business school in Asia. The founders, some of the best minds from the corporate and academic worlds, anticipated the leadership needs of the emerging Asian economies. The ISB is committed to creating such leaders through its cutting-edge programs, outstanding faculty, and thought leadership. The Indian School of Business (ISB) provides a robust environment that generates high-quality research that is both contemporary and rigorous.

Roles and Responsibilities

You will assist the faculty in their ongoing research work which includes literature review, data collection, data management, and empirical analysis.

Core Responsibilities

1. Fetch, clean and maintain large-scale datasets from multiple sources

2. Implement empirical analyses using statistical and econometric methods

3. Support research projects through data preparation, exploratory analysis, and result validation

4.



Translate research questions into data workflows and reproducible code

1. Develop regression models for econometric analysis in Stata, R, or Python

2. Use Linux and Windows HPC systems for handling large datasets

3. Generate output statistics, tables, and visualizations for research projects

1. Collaborate with researchers and stakeholders to refine empirical strategies

2. Assist in interpreting results and linking findings to broader research questions

3. Contribute to reports, presentations, and academic-style drafts

1. Adapt to new datasets, tools, and research domains as needed

2. Support ad hoc analysis requests on quick-turnaround projects

Required Skills and Qualifications Qualifications:

- Master from DSE or ISI or a reputed institution in India or abroad / B Tech from Top engineering institutes or Undergraduate in Economics / MBA from IIM ABCs.

Skills:
- Strong motivation to pursue a career in research in Information Systems.
- Experience working with big data systems, concepts, and tools.
- Strong verbal and written communication skills, with attention to every detail.
- Strong team player with cross-functional skills.
- Background in basic econometrics is highly desired.
- Strong coding proficiency in Python, Stata, R.
- Knowledge of various text mining algorithms and their use cases as well as text pre-processing and normalization techniques.
- Exposure to economic problems and econometric techniques desirable.
- Motivation to learn and a strong work ethic.

Our Commitment Towards you If this role is your true calling, please fill out the form using the below-mentioned link.

Applications will be reviewed on a rolling basis, and only shortlisted candidates will be contacted for the next steps.

Indian School of Business

Timings : Monday- Friday, 08:00 AM IST to 06:00 PM IST

Sector 81, SAS Nagar ,Mohali - 140 306

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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