Ms. Nuzhat Prova | Data Science | Excellence in Research Award
Ms. Nuzhat Prova, Pace University, United States
Ms. Nuzhat Prova is a seasoned Data Scientist with over 12 years of IT experience, including more than 7 years in data science and advanced analytics. Based in New York, she specializes in machine learning, predictive modeling, and data visualization, with extensive expertise in Python, R, SQL, and cloud platforms such as Azure and AWS. Currently at UnitedHealth Group, she leads the development of AI-driven solutions for healthcare optimization. Ms. Prova holds a strong academic foundation from Pace University and is recognized for her impactful work in healthcare data science, cloud migration strategies, and AI integration in compliance with HIPAA standards.
Profile
🎓 Early Academic Pursuits
Ms. Nuzhat Prova’s journey into the world of data science and technology began with a strong academic foundation rooted in analytical thinking and problem-solving. Her passion for mathematics, logic, and data interpretation led her to pursue studies that honed her computational and statistical skills. Her academic journey culminated at Pace University, United States, where she received training that not only deepened her understanding of information systems but also introduced her to advanced concepts in data analysis, machine learning, and artificial intelligence. This academic background provided her with the tools and insights necessary to thrive in a data-driven world and empowered her to take on real-world challenges with scientific rigor.
💼 Professional Endeavors
With more than 12 years of experience in IT, including 7+ years in data science and 5 years in data analysis, Ms. Prova has had a dynamic and impactful career. Her professional journey includes roles of increasing responsibility and technical depth across a range of industries, with a current focus in the healthcare sector. At UnitedHealth Group, one of the largest health insurance companies in the U.S., she has played a key role in designing and deploying data-driven solutions that have real-life implications for public health.
Her work spans end-to-end machine learning model development, cloud data warehouse migrations, and advanced analytics to predict and manage chronic conditions. She has contributed to reducing hospital readmission rates, increasing patient retention, and enhancing preventive care strategies by building sophisticated predictive models using tools such as Python, Scikit-learn, Azure ML, and SQL.
She also actively contributes to data engineering processes, managing large datasets using Apache Spark, Scala, and Azure Data Factory, and supports migration efforts from on-premise systems to Azure and Snowflake, ensuring data security, integrity, and accessibility.
🔍 Contributions and Research Focus
Ms. Prova’s research and professional contributions focus primarily on applying AI and machine learning to improve healthcare outcomes. Her key interests include predictive analytics, chronic disease modeling, natural language processing (NLP) using Transformers and OpenAI APIs, and privacy-compliant data solutions.
One of her standout achievements includes developing churn prediction models that helped increase member retention rates. Additionally, she has worked on retrieval-augmented generation (RAG) architecture for smarter data access and implemented AI search tools that comply with HIPAA and FHIR standards.
Her technical repertoire includes a vast array of tools and frameworks like TensorFlow Federated, SHAP, LIME (for explainability), AutoML, DVC, and Kubeflow, making her a well-rounded data scientist with a research-driven mindset.
🏆 Accolades and Recognition
Throughout her career, Ms. Prova has earned recognition for her exceptional ability to bridge technical expertise with real-world application. Her leadership in cloud migration projects and AI-based healthcare solutions has been acknowledged internally within organizations she’s worked with. Moreover, her improvements in model accuracy—enhancing predictions from 65% to 86% using SVM algorithms—speak volumes about her dedication and technical acumen.
She is often entrusted with high-stakes projects involving sensitive data and cross-functional collaborations, a testament to the confidence and trust she has earned over time. Her professional presence is also reflected through her LinkedIn profile, where she connects with peers, shares insights, and stays updated with the evolving tech landscape.
🌍 Impact and Influence
Ms. Prova’s work in healthcare data science has had a tangible impact on patients’ lives. From reducing healthcare costs through improved forecasting to enhancing treatment pathways, her contributions have been both strategic and humanitarian. She embodies the modern data scientist—technically adept, ethically grounded, and results-oriented.
Her interdisciplinary approach—combining technology, healthcare, and policy compliance—has influenced how data science solutions are designed for the public good. Furthermore, she mentors and collaborates with other professionals, encouraging diversity and inclusion in the tech community.
🛤️ Legacy and Future Contributions
Ms. Prova’s legacy is being shaped by her consistent drive to integrate cutting-edge technologies with meaningful applications. Looking ahead, she aims to deepen her engagement with explainable AI, federated learning, and ethical machine learning frameworks. Her vision includes building intelligent systems that not only optimize performance but also respect user privacy and promote equitable outcomes.
As AI continues to influence healthcare delivery, Ms. Prova is poised to remain at the forefront—guiding the evolution of AI strategies and contributing to policy, governance, and community-based initiatives. Her future endeavors will likely include publishing research, speaking at conferences, and contributing to open-source healthtech platforms.
Publication Top Notes
Healthcare Fraud Detection Using Machine Learning
Deep Learning Approaches for Multi Class Leather Texture Defect Classifcation
Exploring Bengali speech for gender classification: machine learning and deep learning approaches