Data Scientist with Healthcare & Finance Expertise

SUMMARY

Durga Bhavani Prasad Menda

Data Scientist

Jersey City, NJ +1-551-***-**** *************@*****.*** LinkedIn

• 5+ years of experience as a Data Scientist, specializing in developing and implementing predictive models to enhance healthcare and financial analytics.

• Proficient in using machine learning algorithms (XGBoost, SVM, Random Forest) and Bayesian statistical techniques to improve patient outcome predictions, medication adherence, and financial market analysis.

• Experienced in natural language processing (NLP) techniques for analyzing unstructured data, improving sentiment analysis accuracy, and extracting actionable insights from patient feedback and financial reports.

• Skilled in creating interactive dashboards using Tableau and Power BI to visualize and analyze complex data sets, leading to data-driven decision-making and enhanced patient satisfaction.

• Demonstrated expertise in analyzing Electronic Health Records (EHR) and historical financial data using SQL, Hive, and Python (Pandas, Matplotlib, Seaborn), identifying trends, and improving personalized care plans and trading strategies.

• Applied time series forecasting models for predicting demand and market trends, optimizing resource allocation, and reducing shortages in healthcare and financial sectors.

• Utilized clustering algorithms and cohort analysis to segment patients and customers, enabling personalized outreach and improving program participation and screening rates.

• Experienced in developing machine learning pipelines using Scikit-learn, TensorFlow, and PyTorch, and applying ensemble learning techniques to improve model robustness and prediction accuracy in healthcare and financial applications.

SKILLS

Methodologies: SDLC, Agile, Waterfall

Programming Language: Python, SQL, R, SAS, Java

Packages: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn, ggplot2, Plotly, Keras, OpenCV, NLTK, BOW, Word2Vec, Spacy

Visualization Tools: Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP) IDEs: Visual Studio Code, PyCharm, Jupyter Notebook, IntelliJ Cloud Technologies: Amazon Web Services (AWS S3, EMR, Redshift), Microsoft Azure Database: MySQL, Teradata, Oracle DB, Amazon Redshift ML Algorithms: Regression analysis, Bayesian Method, Decision Tree, Random Forests, Support Vector Machine, Neural Network, Sentiment Analysis, K-Means Clustering, KNN and Ensemble Method, Natural Language Processing (NLP)

Other Technical Skills: Predictive Analysis, Time Series Forecasting, Regression Analysis, Hypothesis Testing, Classification, Clustering, Principal Component Analysis (PCA), Deep Learning, Model training and evaluation, Microsoft SQL Server, SSIS, SSRS, SSAS, Hadoop, Spark, Kafka, Snowflake, Redshift, Big Query, Apache Airflow, Informatica, Talend, Jenkins, Kubernetes, Docker, Critical Thinking, Communication Skills, Presentation Skills, Problem-Solving

Version Control Tools: Git, GitHub

Operating Systems: Windows, Linux, Mac iOS

EDUCATION

Master of Science (MS) in Computer Science

Pace University, Seidenberg School of Computer Science & Information Systems, New York, USA Bachelor of Technology (BTech)

CMR Engineering College, Telangana, India

EXPERIENCE

Celgene Corporation, NJ Jun 2023 – Present

Data Scientist

• Developed predictive models using machine learning algorithms (XGBoost, SVM, Random Forest) within AWS SageMaker to improve patient readmission risk assessment by 15%, enabling proactive interventions and reducing hospital readmissions.

• Implemented Bayesian statistical techniques and Python-based machine learning models to predict medication adherence patterns, resulting in targeted interventions that improved patient compliance by 18%.

• Created interactive Tableau dashboards to visualize and analyze patient satisfaction scores across different Celgene Corporation facilities, identifying areas for improvement and contributing to a 10% increase in overall patient satisfaction.

• Applied natural language processing (NLP) and deep learning techniques using TensorFlow and PyTorch to analyze unstructured data from patient feedback forms and Clinical notes, improving sentiment analysis accuracy by 20% and extracting clinical features that supported a 10% boost in patient outcome predictions.

• Analyzed Electronic Health Records (EHR) data using Apache Spark and SQL on cloud platforms (AWS, Azure) to identify trends in chronic diseases, contributing to personalized care plans that reduced complications by 12%.

• Developed time series forecasting models to predict seasonal flu vaccine demand across various locations, reducing vaccine shortages by 20% and optimizing distribution.

• Collaborated cross-functionally with data engineers, clinicians, and stakeholders to architect and implement efficient ETL processes, significantly improving data accuracy and integrity, reducing analysis turnaround time by 30%, and driving analytics solutions that directly support organizational healthcare objectives.

• Performed cohort analysis using SQL and Python (Pandas) to evaluate the effectiveness of preventive care programs, identifying opportunities to increase screening rates by 25%.

• Developed a patient segmentation model using clustering algorithms, enabling personalized outreach strategies that increased participation in wellness programs by 22%.

• Utilized Python libraries (Matplotlib, Seaborn) to visualize and analyze patterns in emergency department utilization, contributing to the implementation of strategies that reduced non-urgent ED visits by 15%. Wipro, India Nov 2018 – Aug 2022

Machine Learning Engineer

• Created an LLM website builder using Lang Chain and Llama Index library, implementing Auto-merging Retrieval with a modular approach.

• Deployed the website builder on GCP Firebase, with projected revenue surpassing $1 million for the company.

• Presented the innovative modular approach for HTML webpage creation at the Forbes CIO conference, generating substantial interest.

• Integrated analytics and monitoring tools into the website builder, enabling real-time tracking of user interactions and behavior to inform iterative improvements and enhancements.

• Performed cross-validation on Auto-merging retrieval window size for LLM metrics using TruLens.

• Designed a scalable chatbot framework using DAG architecture and Llama Index agent's library with React prompting.

• Estimated cost savings of $300,000 with enhanced dialogue management for React chatbot products.

• Designed and implemented A/B tests on the chatbot interface using Google Optimize, resulting in an 8% increase in user retention.

ACADEMIC PROJECTS

Medical Image Analysis: Created algorithms to analyze medical images (e.g., X-rays, MRIs, CT scans) for detecting abnormalities or diagnosing specific conditions like tumors, fractures, or neurological disorders.

• Trained machine learning models for the detection of specific abnormalities in medical images.

• Web-based interface for uploading medical images and obtaining automated predictions.

• Documentation including project overview, dataset description, model architecture, training procedure, and deployment instructions.

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