A Day in the Life of a Gen AI Scientist at Property Software Company
A typical day for a Gen AI Scientist data science manager is "quite varied," involving a mix of hands-on work like "coding or model optimization" and leadership tasks such as team check-ins, project strategy meetings, and collaboration with product teams. This is complemented by continuous learning through research papers and online courses to stay abreast of advancements in the field.
Project Management, Coding, Data Analysis, Communication, Problem-Solving
Advizer Information
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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. A data science manager's day involves a mix of project management, coding/model optimization, and experimentation with different model architectures.
2. Collaboration is key; the role includes meetings to discuss project progress, strategize initiatives, and coordinate with product teams.
3. Continuous learning is emphasized through daily activities such as reading research papers, exploring online courses, and staying updated on advancements in Gen AI and data science fields
Transcript
What does a day in the life of a data science manager look like?
That's a really good question. It totally depends on the day. A typical day for me is quite varied, to be honest.
I might start by checking in with my team on ongoing projects, then probably dive into some coding or model optimization that I'm working on, or some POC that I'm working on. I often spend my time experimenting with different model architectures, conducting some A/B testing on these models, and checking for hallucinations in models that have already been deployed.
Then there are usually meetings scattered throughout the day where we discuss project progress, strategize future initiatives, and have discussions with product teams for some future projects. We plan what we can start ahead of time if we have the bandwidth.
I also try to dedicate some time each day to staying updated on the latest advancements in the Gen AI and data science fields. I do this by reading research papers, newsletters, or going through some courses provided for free from Pluralsight. I also review any new videos about advancements in these fields.
Typically, I end my day by wrapping up some of the projects I'm working on or testing what I've done. I also address any queries or concerns my team has. So yeah, pretty much that's how it is.
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