Shreya Raju

Highly skilled and results-driven AI/Machine Learning Engineer with a demonstrated ability to translate raw data into actionable insights. Possessing a strong foundation in the development and scaling of machine learning models, I have consistently delivered solutions to address intricate business challenges. My expertise spans a diverse range of techniques, from supervised to reinforcement learning, showcasing a creative approach to problem-solving. Proficient in leveraging Power BI for crafting engaging data visualizations, I bring a unique blend of technical acumen and creative thinking to enhance decision-making processes. With a proven track record of successful project implementations, I am eager to contribute my knowledge and skills to a forward-thinking team dedicated to pushing the boundaries of AI and analytics. Committed to staying at the forefront of industry advancements, I am poised to drive innovation and make impactful contributions to the evolving landscape of artificial intelligence.

AI/Machine Learning Project | PNC Bank, Pittsburgh, PA | September 2021 - Present

• Lead a team in implementing advanced AI/ML solutions, applying supervised and unsupervised learning techniques to address intricate business challenges.
• Developed and scaled machine learning models, including Logistic Regression, Random Forest, Gradient Boosting Machines, and SVM, resulting in significant improvements in classification accuracy.
• Automated the ML model building process by implementing efficient Data Pipelines, reducing processing time and enhancing scalability.
• Collaborated closely with business stakeholders to understand complex requirements, utilizing predictive modeling, text mining, and forecasting techniques to provide valuable insights.
• Introduced innovative approaches in data visualization using Power BI, delivering dynamic insights to stakeholders.

Patient Outcome Prediction and Resource Optimization | CVS Healthcare System

• Led a transformative data analytics initiative, leveraging EHR data to predict patient outcomes and optimize resource allocation.
• Analyzed electronic health records (EHR) data to identify patterns and factors influencing patient outcomes, incorporating clinical, demographic, and historical data.
• Developed predictive models leveraging machine learning algorithms to forecast patient admission rates, readmission risks, and potential complications.
• Collaborated with healthcare professionals to integrate predictive analytics into the decision-making process, enabling early identification of high-risk patients and proactive intervention.
• Conducted root cause analysis on readmissions and adverse events, providing actionable insights to improve care protocols and reduce preventable incidents.
• Crafted user-friendly dashboards for healthcare providers to visualize real-time patient data, facilitating informed decision-making at the point of care.

Semiconductor Yield Optimization | Intersil Microsystems

• Headed a cross-functional team, driving improvements in semiconductor yield management processes, resulting in enhanced chip manufacturing efficiency and cost savings.
• Collaborated with engineers and production teams to implement real-time monitoring systems, enabling early detection of potential issues and minimizing production downtime.
• Conducted root cause analysis on manufacturing deviations, utilizing data insights to implement corrective actions and prevent recurrence.
• Utilized machine learning algorithms to predict potential yield bottlenecks, allowing for proactive adjustments in production planning.
• Automated data collection and reporting processes, reducing manual effort by 30% and improving data accuracy.
• Developed visually compelling dashboards using visualization libraries in Python, providing stakeholders with real-time insights into yield performance and key performance indicators (KPIs).
• Presented findings and recommendations to senior management, fostering a data-driven decision-making culture within the organization.
• Collaborated with external vendors to implement advanced sensor technologies, further improving the accuracy of data collection and analysis.