Ramakrishna B

About Ramakrishna B

As a Data Scientist with over 3 years of experience, I bring expertise in developing and deploying machine learning models, advanced analytics, and predictive modeling. I am highly skilled in Python, SQL, R, and SAS, utilizing tools such as NumPy, Pandas, Scikit-learn, TensorFlow, and Keras to deliver data-driven solutions. My background includes building robust data pipelines, performing intricate data preprocessing, and creating impactful visualizations using Tableau and Power BI. I also have significant experience with cloud platforms, especially AWS, which allows me to optimize and scale data processing workflows. I thrive in collaborative environments, translating complex data into strategic insights that drive informed business decisions. My commitment to data quality and integrity is unwavering, and I am passionate about leveraging data to tackle challenging problems and achieve business goals.

Experience

1. Data Scientist

   Centene Corporation

   Aug 2023 — Present · MO, US

   Improved predictive model accuracy by 20% through advanced feature engineering, hyperparameter tuning, and the use of ensemble methods. Developed and maintained robust data preprocessing pipelines using Python, ensuring high-quality and well-prepared data for analysis, and ensuring data quality and integrity through comprehensive validation, cleaning processes and regular data audits. Partnered with healthcare professionals to incorporate domain-specific knowledge into data models, enhancing the relevance and accuracy of insights. Successfully deployed machine learning models into production environments using Docker and Kubernetes, ensuring scalability and reliability. Utilized Python libraries such as Scikit-learn, TensorFlow, and Kera’s for the development of sophisticated machine-learning models. Reduced patient readmission rates by 15% by implementing predictive models using Random Forest and Support Vector Machines (SVM) and increased data processing speed by 30% through the optimization of SQL queries and the efficient use of AWS Redshift. Employed NLP techniques to analyze patient feedback, extracting actionable insights to enhance patient care and satisfaction. Designed and implemented advanced ANN models to predict customer churn, achieving a 15% increase in prediction accuracy compared to traditional models. Developed and optimized CNN architectures for image classification tasks, resulting in a 20% improvement in accuracy over baseline models. Developed custom Transformer-based models for predicting patient health trends from unstructured data, leading to a 10% increase in early detection of high-risk conditions

Education

• University of Central Missouri

  Master's degree

  2023 — 2024