College Experiences That Helped a Gen AI Scientist at Property Software Company Succeed
Gagandeep's undergraduate coursework in information technology, including early exposure to neural networks and foundational data science concepts, provided a strong base for their career in Gen AI. Further, their proactive pursuit of real-world application through internships and research, coupled with focused development of communication skills via presentations and competitions, proved instrumental in bridging the gap between theory and practice and building a strong foundation for career success.
Coding, Data Analysis, Problem-Solving, Communication, Project Management
Advizer Information
Name
Job Title
Company
Undergrad
Grad Programs
Majors
Industries
Job Functions
Traits
Gagandeep Singh
Gen AI Scientist
Property Software Company
Uttar Pradesh Technical University
Arizona State University (ASU) - W. P. Carey
Computer Science
Real Estate, Technology
Data and Analytics
None Applicable
Video Highlights
1. Focus on foundational programming skills (C++, Java): Building a strong base in programming languages is crucial for understanding and developing AI applications.
2. Combine theoretical knowledge with practical experience: Seek opportunities to apply your learning through internships, research assistant roles, or personal projects to bridge the gap between theory and practice.
3. Develop strong communication skills: Practice explaining complex technical concepts clearly and concisely through presentations, case competitions, or any other opportunities to build confidence and effective communication skills
Transcript
What did you do in undergrad to set you up for success in your career?
I did my undergrad in Information Technology from India. During those four years, from 2010 to 2014, we primarily worked with traditional programming languages like C, C++, and Java as part of the curriculum.
This gave me exposure to coding and helped me develop logical thinking. Learning to translate my thoughts into code was crucial and contributed significantly to my growth.
Apart from that, we had subjects like neural networks, which provided foundational skills in data science, machine learning, and AI. This early exposure to these concepts was helpful in learning new technologies, especially deep learning tools, because deep learning is all about neurons and building deep neural networks.
After my undergrad, my master's program significantly contributed to my career success by bridging the gap between theory and practice. I actively sought opportunities to apply what I learned to real-world problems during both my undergraduate and master's degrees.
For instance, during my undergrad, I undertook internships where we implemented traditional software development and engineering practices to solve real-world problems. My role as a research assistant at the university was also instrumental.
While working on projects like multilevel tech classification, using these work technologies allowed me to apply cutting-edge techniques to solve business problems.
Lastly, I made sure to develop my communication skills. I volunteered to present findings at meetings and participated in case competitions during both my undergrad and master's. These experiences honed my ability to explain complex technical concepts.
I would say this particular skill has been crucial in my roles as a team leader and manager. All these efforts during my undergraduate and master's programs laid a strong foundation for my career progress, not only in my technical skills but also in my soft skills.
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