Machine Learning Data Scientist

Sriharsha Dugganaboina

*********.************@*****.*** +1-609-***-**** www.linkedin.com/in/sriharsha-dugganaboina Professional Summary

Analytical and results-oriented Data Scientist with expertise in Machine Learning, Mechanical Systems, and Data Analysis. Experienced in developing predictive maintenance models and performing vibration analysis for mechanical systems, utilizing tools like Python, MATLAB, and SQL. Proficient in time-series forecasting, signal processing, and Finite Element Analysis (FEA) to optimize mechanical designs and monitor system performance. Skilled in creating interactive dashboards using Power BI and Tableau to visualize machine health, energy efficiency, and failure trends, enabling data-driven decision-making. Strong collaborator with experience in working cross-functionally to integrate complex data sets and drive actionable insights. Education

Master of Science – Information Systems January 2023 – December 2024 Pace University – New York City, New York

GPA: 3.97/4.0

Bachelor of Technology – Mechanical Engineering June 2018 – July 2022 Jawaharlal Nehru Technological University, Anantapur Technical Skills

Programming Languages & Tools:

• Python: pandas, NumPy, matplotlib, seaborn, scikit-learn, PySpark, TensorFlow, Keras

• SQL: BigQuery, MySQL, PostgreSQL

• Data Visualization: Tableau, Power BI

• Big Data Frameworks: Hadoop, Spark, Databricks

• Mechanical Engineering Tools: MATLAB, Finite Element Analysis (FEA)

• MS Office: Excel, Word, PowerPoint

Data Analysis & Modeling:

• Relational Database Design and Implementation

• Statistical Analysis and Hypothesis Testing

• A/B Testing and Experimentation

• Machine Learning: Regression, Classification, Clustering, and Time Series Analysis Cloud Platforms:

• Google Cloud Platform

• AWS (Redshift, S3, EC2)

• Microsoft Azure

Professional Experience

Bristol Myers Squibb Data Scientist Intern (January 2024 – Current)

• Performed in-depth analysis of clinical trial and patient datasets comprising over 150,000 records, identifying key trends that enhanced drug efficacy insights by 15%, leading to improved treatment strategies.

• Designed and implemented predictive machine learning models with an 85% accuracy rate, enabling the forecasting of patient treatment outcomes and early detection of at-risk groups, improving proactive healthcare interventions.

• Developed and automated interactive dashboards in Power BI/Tableau, streamlining the visualization of complex healthcare data and reducing data reporting time by 30%, allowing stakeholders to make faster and more informed decisions.

• Collaborated with cross-functional teams, including clinicians, data engineers, and supply chain analysts, to optimize drug distribution networks, achieving a 20% reduction in logistics costs through data-driven recommendations.

• Applied clustering algorithms (such as K-Means and DBSCAN) to segment 50,000+ patients, allowing healthcare providers to personalize treatment plans, leading to a 10% increase in patient satisfaction and improved clinical outcomes.

• Conducted feature engineering and data preprocessing, handling missing values, normalizing datasets, and performing statistical transformations to enhance model accuracy and robustness.

• Implemented A/B testing and statistical analysis techniques to evaluate the effectiveness of treatment methodologies, ensuring evidence-based improvements in clinical procedures.

• Integrated external healthcare datasets, such as demographic and environmental factors, into predictive models, improving their ability to capture real-world patient conditions and provide more accurate forecasts.

• Optimized SQL queries and data pipelines to improve data retrieval efficiency, reducing processing time by 40%, and enabling seamless integration of patient records for real-time analytics.

• Presented key findings and insights to stakeholders through reports, executive presentations, and interactive visualizations, facilitating data-driven decision-making in healthcare operations and clinical research. Bosch ML for Mechanical Systems Internship (June 2021 - Nov 2022)

• Developed Predictive Maintenance Models using time-series forecasting and statistical methods to analyze sensor data and prevent mechanical failures.

• Implemented Vibration Analysis Techniques to monitor rotating machinery performance, using Fourier Transform (FFT) and signal processing.

• Optimized Mechanical Designs through Finite Element Analysis (FEA) visualizing efficiency improvements in Power BI/Tableau reports.

• Performed Data-Driven Performance Analysis of mechanical components using MATLAB, Python, and SQL, integrating results with BI tools for trend analysis and reporting.

• Created Interactive BI Dashboards to monitor machine health, energy efficiency, and failure trends, helping engineers make data-driven decisions.

• Utilized Signal Processing Techniques such as Fast Fourier Transform (FFT) and wavelet analysis for machine condition monitoring and displayed findings in BI reports.

• Reduced Equipment Downtime by 15% by implementing predictive maintenance strategies and tracking insights using Power BI dashboards.

• Improved Operational Efficiency by 20% through data-driven mechanical optimizations, leveraging BI tools for performance reporting and anomaly detection.

Academic Projects

• HCP Performance Dashboard (Tableau) – Developed an interactive Tableau dashboard to assess HCP performance across 10+ metrics, improving patient care insights and operational efficiency, while reducing manual reporting effort by 30%.

• Sales Performance Dashboard (Power BI) – Built a Power BI dashboard to track sales metrics across multiple regions and products, leveraging DAX functions and custom visualizations to enhance decision-making by 25% and improve salesperson performance analysis.

• Stock Price Prediction Model – Designed a Keras-based time-series model to predict stock prices with 2.5% accuracy, incorporating moving averages and Min-Max scaling to enhance model performance and reduce RMSE by 10%. Certifications

• Microsoft Certified: Power BI Data Analyst Associate

• Google Professional Certificate: Project Management

• Google: Data Analytics Professional

• Python Developer course by Udemy

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