Meghana Kota

About Meghana Kota

I am a results-driven Data Scientist passionate about building intelligent systems that transform complex data into meaningful business and societal impact.With 4+ years of experience across healthcare and financial analytics, I specialize in developing scalable machine learning solutions, deep learning models, and AI-driven decision systems that improve operational efficiency, risk mitigation, and strategic planning.At Pfizer, I work on advanced clinical analytics, medical imaging intelligence, and Generative AI powered knowledge retrieval systems that enhance research productivity and data-driven healthcare decisions at scale. Previously at Accenture, I led large-scale fraud risk analytics initiatives, processing multi-terabyte transactional datasets and delivering predictive models that significantly improved detection accuracy and regulatory compliance.My expertise spans machine learning, statistical experimentation, deep learning architectures, time-series forecasting, and cloud-native AI deployment. I enjoy solving ambiguous business problems, influencing cross-functional stakeholders, and translating analytical insights into measurable impact.I am particularly interested in opportunities focused on Applied AI, Responsible AI, and large-scale predictive intelligence where innovation, ownership, and real-world impact intersect.Core Competencies:• Machine Learning & Deep Learning• Generative AI, LLMs & RAG Systems• NLP & Time Series Forecasting• Python, SQL, Apache Spark• AWS & Azure AI Solutions• Statistical Modeling & Experimentation• Data Storytelling & Executive CommunicationLet’s connect if you are building the future with data and AI.

Experience

1. Data Scientist

   辉瑞

   Aug 2024 — Present · TX, US

   Designed and deployed scalable end-to-end machine learning pipelines in Python, improving data preprocessing efficiency and accelerating predictive modeling workflows across global clinical research initiativesBuilt deep learning computer vision models (CNN architectures) for medical imaging analytics, significantly improving anomaly detection accuracy and enabling faster clinical insights for research teamsDeveloped Generative AI powered Retrieval-Augmented Generation (RAG) systems integrating structured and unstructured clinical knowledge sources to enhance AI-assisted decision supportPerformed advanced statistical experimentation and hypothesis testing on patient datasets to validate model performance, reduce false positives, and strengthen analytical reliabilityCreated executive-level interactive dashboards in Tableau improving visibility into patient outcomes, research KPIs, and operational performance metricsLed deployment of cloud-native analytics solutions on AWS, enabling scalable model inference, optimized latency, and secure access for distributed stakeholdersOptimized complex PostgreSQL data models and queries supporting large clinical datasets and improving reporting turnaround timeChampioned reproducible data science workflows using Git-based version control and Linux environments, enhancing collaboration, deployment stability, and model governance

Education

• University of North Texas

  Master's degree, Advanced Data Analytics

• JNTUH College of Engineering Jagityala

  Bachelor of Technology - BTech, Electronics and Communications Engineering