Influential Woman · AI and machine learning
Shweta Sharma
Data Analyst CO-OP (Data Science), American Board of Internal Medicine
Philadelphia, PA 19106
Her Story
About Shweta
Data and product analytics professional with experience spanning NLP, data science, UX research, and behavioral analytics across healthcare, education, and technology. She currently works as a Data Analyst Co-op at ABIM, building NLP and LLM evaluation pipelines for healthcare communication and bias detection, while also serving as a Course Assistant at Drexel. Her background includes research and analytics leadership at John Deere, where she drove experimentation, dashboarding, and product insights, along with earlier roles in UX, web analytics, personalization, and user behavior analysis. She brings a strong mix of machine learning, experimentation, product thinking, and user-centered research.
Her Interview
Ten minutes with Shweta
01What do you attribute your success to?
A blend of technical curiosity and empathy. My background in UX research allows me to see the human story behind the data, while my technical skills in NLP and data science allow me to build the solutions that address those human needs.
02What’s the best career advice you’ve ever received?
Don't just provide data; provide insights. Numbers are meaningless unless they are translated into a narrative that can drive decision-making and improve the user experience.
03What advice would you give to young women entering your industry?
Advocate for the ethical side of technology. As AI becomes more prevalent, we need more women in the room to ensure bias detection and healthcare communication are handled with precision and care.
04What are the biggest challenges or opportunities in your field right now?
The biggest opportunity lies in LLM evaluation—specifically making AI safer and more accurate in sensitive fields like healthcare. The challenge is ensuring these models are inclusive and free from algorithmic bias.
05What values are most important to you in your work and personal life?
Integrity in data reporting, continuous learning, and a commitment to using technology as a tool for social and professional equity.
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