In this podcast episode, we talked with Lavanya Gupta about Building a Strong Career in Data.
About the Speaker:
Lavanya is a Carnegie Mellon University (CMU) alumni of the Language Technologies Institute (LTI). She works as a Sr. AI/ML Applied Associate at JPMorgan Chase in their specialized Machine Learning Center of Excellence (MLCOE) vertical. Her latest research on long-context evaluation of LLMs was published in EMNLP 2024.
In addition to having a strong industrial research background of 5+ years, she is also an enthusiastic technical speaker. She has delivered talks at events such as Women in Data Science (WiDS) 2021, PyData, Illuminate AI 2021, TensorFlow User Group (TFUG), and MindHack! Summit. She also serves as a reviewer at top-tier NLP conferences (NeurIPS 2024, ICLR 2025, NAACL 2025). Additionally, through her collaborations with various prestigious organizations, like Anita BOrg and Women in Coding and Data Science (WiCDS), she is committed to mentoring aspiring machine learning enthusiasts.
In this episode, we talk about Lavanya Gupta’s journey from software engineer to AI researcher. She shares how hackathons sparked her passion for machine learning, her transition into NLP, and her current work benchmarking large language models in finance. Tune in for practical insights on building a strong data career and navigating the evolving AI landscape.
🕒 TIMECODES
00:00 Lavanya’s journey from software engineer to AI researcher
10:15 Benchmarking long context language models
12:36 Limitations of large context models in real domains
14:54 Handling large documents and publishing research in industry
19:45 Building a data science career: publications, motivation, and mentorship
25:01 Self-learning, hackathons, and networking
33:24 Community work and Kaggle projects
37:32 Mentorship and open-ended guidance
51:28 Building a strong data science portfolio
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