Work Experience.
This page highlights my work in data science, analytics, and user experience (UX) across internships, research, and applied projects. While my core expertise lies in machine learning (ML), natural language processing (NLP), and statistical modeling using Python, I developed a strong interest in UX research and design during my Master's, which has grounded my technical work in user-centered thinking.
2025
PUBLICATION
This work demonstrates the value of explainable machine learning in addressing workforce well-being, operational efficiency, and healthcare delivery. By identifying key drivers of depression and PTSD, our models provide actionable insights for healthcare systems, policymakers, and employers. The findings offer a strategic framework for integrating interpretable AI into mental health diagnostics and employee experience platforms.
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This research was a part of my work under UNC's Department of Radiation Oncology, and will be presented at MedInfo 2025 (Taipei, Taiwan; August 2025) and the Total Worker Health Symposium (Bethesda, Maryland, USA; October 2025), highlighting its impact across global health and occupational safety communities.
2024
April 2024 - December 2024
As a Data Scientist at UNC Libraries, I Led data pipeline development for On the Books: Algorithms of Resistance, a machine learning initiative uncovering racially discriminatory Jim Crow laws.
I transformed 100+ law volumes from three Southern U.S states into structured, searchable datasets, now used by educators and researchers to analyze systemic bias and support policy reform.
June 2024 - August 2024
Exponentia.ai is a Mumbai-based AI consulting firm that builds enterprise-grade data science solutions across finance, healthcare, and retail.
During my internship at Exponentia, I built scalable ML pipelines and integrated large language models (LLMs) to extract actionable insights from unstructured enterprise data across finance and healthcare domains. My work automated routine analytics and reporting tasks, accelerated the turnaround time for client deliverables, and enabled real-time personalization of insights for end users. This contributed to advancing the firm’s ability to operationalize LLMs.
2022
May 2022 - July 2022
PwC (PricewaterhouseCoopers) is a global leader in consulting and professional services.
As part of the Advisory – Technology Consulting team in Kolkata, I designed and developed a voice-activated business intelligence assistant. This project was one of the team’s earliest forays into conversational AI that enabled audit teams to query performance metrics in natural language instead of navigating static dashboards.
Built in Python using Google Cloud services and custom NLP pipelines (pre-ChatGPT era), the tool transformed tabular data into dynamic voice-based summaries, significantly reducing manual reporting effort and reshaping how teams accessed insights. This prototype laid foundational work for future user-first analytics products at the firm.
September 2024 - May 2025
At Duke University, I collaborated with the Assessment & User Experience Strategy (AUXS) team and Fuqua School of Business to improve internal digital platforms through UX research and evidence-based design.
I led usability testing, conducted behavioral analysis, and drove redesigns of staff-facing systems, which resulted in higher task completion rates and measurable gains in overall platform usability.
Modeling Workplace Predictors of Depression and PTSD in Healthcare Professionals Using Machine Learning
Anjali Yellapuntula Venketa, Karthik Adapa, Lukasz Mazur