Miss Smita Raghavendra Bhat

Smita Bhat is a highly accomplished analytics professional with a robust background in data science, engineering, and business planning. She possesses a Master of Science in Analytics from the University of Southern California, where her coursework spanned critical domains like statistics, fraud analytics, and optimization methods. Complementing this, she holds a Bachelor of Engineering in Electronics and Communication from Sri Jayachamarajendra College of Engineering, Mysuru, with a strong foundation in signal processing and advanced algorithms. Her professional journey began as a Data Analyst Intern at Keck Medicine of USC, where she leveraged Hive SQL and Tableau to extract, analyze, and visualize critical data for auditing ETL processes. This hands-on experience equipped her with a deep understanding of data pipelines and quality assurance. She further honed her analytics skills during a consulting practicum at Kiana Analytics, where she developed an anomaly detection framework for Rio de Janeiro International Airport, demonstrating her ability to handle large datasets and deliver innovative solutions. At Tesla, as a Charging Engineering Intern, Smita showcased her prowess in data engineering and machine learning. She automated classification systems for Supercharger failures using PySpark, significantly improving operational efficiency. Her work, combining SQL analysis and impactful visualizations, was instrumental in identifying key failure patterns, directly influencing service enhancements. Smita's career trajectory at Advanced Micro Devices (AMD) reflects her growth as a data scientist and leader. Initially, as a Product Development Engineer 2, she employed machine learning to diagnose production inefficiencies, reducing analysis time by 60%. Her innovations, including a scalable agent framework and a component binning tool, earned her two prestigious Spotlight Awards. Promoted to Business Planning Manager, she applied advanced analytics to revenue growth strategies and automated reporting systems, eliminating inefficiencies and driving decision-making processes. Her expertise extends across programming (Python, R, PySpark), data engineering (Snowflake, DBT, Docker), and visualization tools (Tableau, Power BI). Smita is adept at statistical analysis, having implemented ANOVA and non-parametric techniques to identify yield inhibitors and statistical drifts. Her ability to enhance database performance, as demonstrated by her SQL optimizations at AMD, underscores her technical acumen. Smita's leadership roles further amplify her profile. As a Senator for the Viterbi Graduate Student Association, she organized multiple networking and social events, fostering community engagement. Her volunteer work with ‘Project Reach Out’ highlights her commitment to social impact, having spearheaded fundraising and awareness programs. Beyond professional and academic excellence, Smita's projects, such as a credit card fraud detection model and energy load forecasting using time series analysis, illustrate her passion for solving real-world problems. Her innovative solutions, like deploying ensemble models and achieving a MAPE of 4.01% in energy prediction, showcase her ability to merge theoretical knowledge with practical applications. In summary, Smita Bhat embodies a blend of technical expertise, innovative problem-solving, and leadership. Her extensive experience across diverse industries, coupled with her commitment to driving impactful solutions, positions her as a transformative force in the analytics domain. Her ability to adapt to evolving technologies while maintaining a focus on efficiency and precision makes her an invaluable asset to any organization.

• machine learning
• Data Analytics
• Data Engineering
• Python Programming
• Database Management
• Data Science
• Product Development
• Data visualization and forecasting
• R Programming
• Statistical Modeling
• Statistical analysis
• Anomaly Detection
• Optimization methods
• Time Series Analysis
• Business Planning
• Data Architecture
• SQL Optimization
• PySpark Proficiency
• Snowflake Expertise
• Docker Usage
• Tableau Skills
• Power BI Visualization
• Fraud Analytics
• Feature Engineering
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