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Click here or scroll down to respond to this candidateCandidate's Name
Lawrenceville, GeorgiaPHONE NUMBER AVAILABLE EMAIL AVAILABLE LinkedInOVERVIEW 4+ years of experience Data Analyst with extensive experience in the healthcare and financial sectors. Proficient in managing and analyzing large-scale datasets, including EHR, EMR, claims, clinical, and transactional data. Expertise in developing predictive models and deploying machine learning algorithms using Python libraries such as ScikitLearn, TensorFlow and AWS Sage Maker. Adept at building interactive dashboards in Power BI and Tableau to deliver Real-time insights, enhancing decision-making processes. Strong background in SQL optimization, ETL pipeline design with SSIS and Informatica, and automation of data preprocessing with Python (Pandas, NumPy). Proven track record in ensuring compliance with HIPAA, GDPR and PCI-DSS regulations, securing sensitive data and maintaining data quality through advanced security protocols. Experienced in conducting A/B testing, hypothesis testing and advanced statistical analysis to support data-driven healthcare and financial initiatives. Highly collaborative, working closely with cross-functional teams in Agile environments to drive operational efficiency and achieve strategic business outcomes. Skilled in advanced Excel functions, developing KPIs, and utilizing a wide range of analytical tools and techniques for data extraction, transformation and reporting.EDUCATIONMaster of Science in Management Information Systems Aug 2022 - May 2024 UNIVERSITY OF HOUSTON, C. T. Bauer College of Business, Houston, TX Major in, GPA: 3.56 / 4.0Bachelor of Technology in Electronics and Communications Aug 2016 - May 2020 JAIN UNIVERSITY, Bengaluru, India Major in, GPA: 3.4 / 4.0TECHNICAL SKILLSMethodologies: SDLC, Agile, Waterfall.Programming Language: Python, SQL, Scala, R.Packages: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn. Visualization Tools: Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP). IDEs: Visual Studio Code, PyCharm, Jupiter Notebook. Cloud Platforms: AWS (Amazon web services).Database: MySQL, PostgreSQL, MongoDB, T-SQL.Other Technical Skills: Google Analytics, DAX, SAS, JIRA, SAP, SSIS, SSRS, Machine Learning Algorithms, Mathematics, Probability distributions, Confidence Intervals, Hypothesis Testing, Regression Analysis, Linear Algebra, advanced analytics, Data Mining, Data Visualization, Data warehousing, Data transformation, Data Storytelling, Association rules, Clustering, Classification, Regression, A/B Testing, Forecasting and Modelling, Data Cleaning, Data Wrangling, Process Mapping, Solution Oriented, Ad Hoc Analysis, Project Management, Data Presentation, Requirement Gathering, Root Cause Analysis, Data Sets, Data Modules, Quantitative Analytics. Version Control Tools: Git, GitHub.Operating Systems: Windows, Linux, Mac iOS.PROFESSIONAL EXPERIENCEData Analyst McKesson, Texas, USA Aug 2023 Present Managed and analyzed large-scale healthcare datasets (EHR, EMR, Claims, and Clinical Data) to optimize patient flow and resource allocation, enhancing operational efficiency by 20% and reducing patient wait time. Developed and deployed predictive models using Python libraries (Scikit-Learn, TensorFlow) to forecast patient readmissions, achieving a 15% reduction in readmission rates across multiple departments. Designed interactive Power BI dashboards that provided real-time insights into patient outcomes, clinical workflows, and treatment efficacy, leading to a 25% improvement in care quality and a 10% increase in treatment adherence. Led the implementation of HIPAA-compliant data quality assurance protocols, encryption standards, and security audits, maintaining 100% compliance and securing sensitive patient information from unauthorized access. Optimized complex SQL queries to enhance data extraction from large relational healthcare databases, improving query performance by 40% and cutting down report generation time. Automated end-to-end data cleaning and preprocessing pipelines using Python (Pandas, NumPy), reducing manual data preparation effort by 50% and improving overall data accuracy. Applied advanced Excel functions (Pivot Tables, VLOOKUP, Macros) to generate accurate financial and clinical reports, increasing reporting accuracy by 15% and shortening report delivery time. Conducted A/B testing on various treatment plans, providing data-driven insights that resulted in a 12% improvement in patient recovery rates and a 10% reduction in medication errors. Generated KPIs and healthcare metrics such as patient satisfaction, treatment adherence and operational efficiency, enabling data- driven decision-making for hospital administrators and healthcare providers. Deployed machine learning models on AWS Sage Maker to enhance predictive analytics for patient outcomes, improving model accuracy and enabling proactive interventions for high-risk patients. Designed and maintained robust ETL pipelines using SSIS and Informatica to streamline data integration and processing, reducing data latency and improving data availability for real-time analytics. Performed hypothesis testing and advanced statistical analysis (ANOVA, regression models) on healthcare initiatives, contributing to reduction in operational costs while maintaining high standards of patient care. Data Analyst Accenture, India May 2019 - Jul 2022 Involved in developing a fraud detection classification model that achieved 95% accuracy, significantly reducing false transactions and enhancing overall security in collaboration with data science teams. Analyzed transaction data from over 1 million financial accounts and implemented algorithms that reduced false positives by 20%, resulting in fewer flagged transactions requiring manual review. Designed and developed interactive Tableau dashboards to track real-time transaction patterns and fraud trends, leading to a 30% improvement in fraud detection efficiency. Implemented robust encryption protocols in compliance with GDPR and PCI-DSS financial regulations, protecting sensitive customer information and reducing security breach risks by 15%. Cleaned and preprocessed over 250GB of financial transaction data from multiple sources using Python and SQL, ensuring accuracy and consistency across multiple reporting systems. Developed and deployed machine learning algorithms, including logistic regression and random forest models, for predictive fraud detection, resulting in a 25% increase in detection speed. Collaborated closely with IT and DevOps teams to successfully integrate the fraud detection system into legacy banking software, ensuring seamless data flow and minimal disruption to existing services. Conducted extensive data mining on large-scale financial datasets using Python and SQL to identify hidden patterns & anomalies, contributing to a 25% increase in the early detection of fraudulent activities and improving overall fraud prevention strategies. Utilized SQL and Python to extract, transform, and analyze complex financial datasets, improving query performance by 40%, and leading to faster data retrieval and decision-making. Performed comprehensive risk assessments across various banking processes, identifying potential vulnerabilities, and providing actionable recommendations that reduced fraud risks by 18%. Presented key insights and data-driven recommendations to stakeholders, influencing policy updates that strengthened fraud prevention and improved data compliance protocols. Worked on cross-functional teams using Agile methodologies to manage data analysis projects, ensuring on-time delivery of fraud detection models and enhancing team productivity by 20% through improved workflow processes. |
