Rama K
Senior Data Scientist @Cardinal Health
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WORK HISTORY
Senior Data Scientist @Cardinal Health
Dublin, OH, US
Designed and deployed AI-powered predictive analytics systems leveraging Azure ML, Databricks, and PySpark to forecast product demand and optimize operations.• Architected end-to-end ML pipelines integrating Azure Data Factory, Synapse Analytics, and Delta Lake for seamless data ingestion and transformation.• Developed deep learning models using TensorFlow and PyTorch to predict production yield and identify potential supply chain risks.• Automated model retraining, validation, and deployment through MLflow and Azure DevOps CI/CD workflows.• Created model governance dashboards enabling continuous tracking, audit, and monitoring of model health and lineage.• Implemented data versioning, feature engineering, and drift detection frameworks integrated with Evidently AI and Prometheus.• Built interactive Power BI dashboards visualizing ML predictions, model confidence intervals, and performance metrics.• Partnered with DevOps teams to containerize ML workloads using Docker and deploy via Azure Kubernetes Service (AKS).• Established data validation frameworks using Great Expectations and automated anomaly detection workflows.• Guided cross-functional teams in adopting cloud-first AI strategies aligned with enterprise transformation goals.• Integrated APIs for real-time inference and feedback loops using FastAPI and Flask microservices.• Orchestrated scheduled pipeline executions using Airflow for continuous data refresh and retraining.• Collaborated with data architects to enhance data quality, cataloging, and observability frameworks across domains.
EDUCATION
JNTUH College of Engineering Hyderabad
Bachelor's degree, Computer Science
ABOUT RAMA K
Accomplished Senior Data Scientist/Machine Learning with over 10 years of experience driving innovation• through data-driven strategies, advanced analytics, and machine learning across global enterprise ecosystems.• Architected and developed scalable data science frameworks integrating ML, AI, and automation pipelines across cloud-native environments (Azure, AWS, GCP).• Designed and optimized data ingestion, transformation, and orchestration pipelines using PySpark, Databricks, Airflow, and modern ETL frameworks for multi-source integration.• Built and deployed predictive, prescriptive, and deep learning models using Tensor Flow, PyTorch, Scikit-learn and ML flow to solve real-world business problems at scale.• Developed end-to-end MLOps solutions integrating CI/CD pipelines with containerized model deployment using Docker, Kubernetes, and cloud automation tools.• Implemented data lake house architectures leveraging Delta Lake, Big Query, and Synapse for unified analytics and improved model reproducibility.• Engineered feature stores, data catalogs, and governance frameworks ensuring compliance, traceability, and reliability in production ML systems.• Integrated NLP, image analytics, and recommender systems using advanced AI frameworks and cloud services to enhance business decision-making capabilities.• Created real-time monitoring frameworks using Evidently AI, Prometheus, and CloudWatch for modelperformance and drift management.• Automated data validation and lineage tracking using Great Expectations and metadata frameworks fortrustworthy analytics.• Partnered with cross-functional engineering, business, and product teams to translate high-level business goals into measurable machine learning outcomes.• Mentored teams in MLOps, DataOps, and AI best practices, establishing reusable components and consistent deployment standards.• Spearheaded multi-cloud data migration and hybrid ML strategies, reducing technical debt and improvingoperational efficiency.• Led end-to-end lifecycle management from data exploration to production deployment and performanceoptimization across complex systems.• Recognized for consistently delivering enterprise-scale AI initiatives that improve efficiency, reduce risk, and drive data-informed decision-making.
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