Shruthi Reddy Vudem
Data Scientist | AI/ML Engineer | Health Data Science
MS Data Science candidate specializing in Health Data Science with expertise in AI/ML, deep learning, and clinical analytics. Passionate about leveraging data science to drive healthcare innovation and improve patient outcomes through predictive modeling and advanced analytics.
Work Experience
Saint Louis University, USA
- • Designed and implemented deep learning models and reliability trend projections (CNN, Transformer) improving model accuracy by 15%
- • Orchestrated scalable ETL pipelines supporting site operations and forecasting, reducing data cleaning time by 30%
- • Integrated utility usage data and trend analysis into models to support forecasting and financial strategy driven healthcare solutions
Excelerate, USA
- • Analyzing consumer behavior patterns using machine learning techniques (clustering, regression, classification)
- • Applying NLP and AI methods to extract actionable insights from unstructured feedback and usage data
- • Collaborating on AI-powered dashboards for real-time trend detection using Python, SQL, and Power BI
Excelerate, USA
- • Optimized and created interactive dashboards (Tableau, Power BI, Looker) improving decision-making speed by 20%
- • Built robust ETL pipelines using Python (Pandas, NumPy), SQL, and AWS Glue
- • Developed predictive analytics and utility forecasting models to optimize business strategy
Waterley Pharmaceuticals, INDIA
- • Applied AI-driven predictive models for equipment failure and utility volume projections, improving accuracy to 85%
- • Modeled operational data to support billing accuracy and manpower utilization forecasting, reducing execution time by 40%
- • Created data visualization for KPI and compliance reporting for biostatistics and decision-making
Technical Skills
Projects
Built predictive models using parallelized Python pipelines for overdose death analysis (2012–2023), achieving 91% accuracy with interpretability features.
Engineered a classification pipeline with Logistic Regression, LASSO, and Random Forest to predict ride cancellations, optimizing features for better accuracy.
Applied regression analysis to 8000+ GSS records, studying correlations between screen time, mental health, and academic performance.
Developed a clinical dashboard to analyze the gap between perceived and actual balance ability in Parkinson's patients, using machine learning for clinical insights.
Analyzed ACS public data to uncover trends in transportation behavior, applying regression models to guide urban planning strategies.
Developed an interactive Power BI dashboard to analyze tech career trends across demographics and skill sets, delivering actionable insights to stakeholders.
Education
Health Data Science
Saint Louis University, Saint Louis, USA
Panineeya Institute of Sciences, INDIA
Publications & Research
Ongoing Research, Saint Louis University
Analyzing 250+ fMRI scans and movement disorder datasets using Python, TensorFlow, and predictive modeling, improving early detection accuracy by 20%.
Applying SQL, Tableau, and Power BI to generate insights aiding clinical decision-making in neurology research.
Published in International Journal of Early Childhood Education
Aug 2022
Conducted research on longitudinal clinical datasets using biostatistical analysis, clinical data mining, and machine learning techniques to evaluate treatment efficacy in endodontics.
Panineeya Institute of Sciences, INDIA (Jan 2022 – Aug 2022)
- • Co-authored a peer-reviewed clinical research paper published in the International Journal of Early Childhood Education
- • Supported the application of statistical modeling, data wrangling, and outcome analysis using SPSS and R to drive data-driven insights in clinical research
Certifications & Training
LinkedIn Learning
LinkedIn Learning
LinkedIn Learning
Research Ethics