| | | 20,000+ Fresh Resumes Monthly | |
|
|
| |
Click here or scroll down to respond to this candidate Candidate's Name
EMAIL AVAILABLE PHONE NUMBER AVAILABLE
SUMMARY
A data-driven analyst with 2+ years of experience bridging healthcare and technology. Skilled in data
acquisition, cleaning, analysis, and visualization using SQL, Python, Excel, and Power BI.
Proven ability to translate complex data into actionable insights that drive informed decision-making. Eager
to leverage expertise in machine learning to contribute to a data-centric organization.
Expert in creating T-SQL objects, working with DDL/DML, perform most of the SQL Server Enterprise
Manager and Management studio functionality using T-SQL Scripts and Batches.
Experience in writing Distributed Queries between SQL Servers.
Handled huge financial data, expert-level query optimization and performance tuning experience.
My experience includes leading Data Management projects and developing Predictive Machine learning
models to derive actionable insights in healthcare settings.
I am also skilled in Quality Assurance methodologies, ensuring regulatory compliance and adherence to
industry standards.
With a proven track record of successfully leading academic and professional projects, including diabetes
prediction using AI and comprehensive analysis of EHR systems like Epic and Cerner. I am adept at
translating data into tangible solutions that improve patient outcomes.
My expertise in Data Analysis and Visualization enables me to translate insights into actionable design
decisions. Organized professional with excellent Oral and written communication skills.
Skilled at assessing client needs, working in groups, suggesting ideas that enhance efficiency and maximize
performance, implementing technology solutions, and training /supporting end users.
I possess hands-on experience with cloud-based data platforms like Databricks and Snowflake, as well as
on-premises solutions such as Teradata and ability to effectively manage data across diverse environments
and extract valuable insights for informed decision-making.
Expertise in database activities like Data Modeling, Database Design, Development, Database creation and
Maintenance, Performance Monitoring and Tuning, Troubleshooting, Normalization, Replication and
Documentation.
Worked in Agile and Scrum environments and extensively utilized OOD and Design Patterns.
Implemented Agile Methodology and performed extensive requirements analysis and requirement
gathering, including data analysis.
Supports cloud architects and business solution architects by improving processes and leveraging agile
frameworks. Design strategy and best practice for Azure Government Cloud with Microsoft team
Experience in installing and configuring Kickstart in Linux.
My experience working with data from various sources and research projects on predicting diabetes and
analyzing Health IT systems demonstrates my ability to collect, analyze, and interpret data to inform
decision-making.
Experience in using the various Machine Learning Techniques, also advance EDA (exploratory data
analysis). used python, R and SAS imp for cleaning the dataset and KNIME for modeling, Tableau in
creating advance visuals and dash boards.
Leveraging data visualization, cleaning, machine learning (including deep learning), and CBR for data
collection, prediction, and diagnosis using neural networks.
EDUCATION:
Masters: Health Informatics 2023
MICHIGAN TECHNOLOGICAL UNIVERSITY, Houghton, MI
MS Graduate Certificates in Public Health Informatics, Artificial intelligence in healthcare & Foundation
in Health Informatics
Bachelors: Pharmacy 2020
JNTU Kakinada University| St. Mary s group of institutions, Guntur, India.
TECHNICAL SKILLS :
Languages PYTHON, SQL, R , NLP, SAS.
Database& SAS, Ms. Excel, Power BI, Tableau, KNIME, Visual Studio, MS-
Visualization Access
Regression, Classification, Exploratory Analysis, Deep learning,
Analytics
Machine learning.
Cloud Based Data Bricks, Snowflakes, Teradata.
Healthcare HIMSS Member, SNOMED CT, LOINC, Rx Norm, GIS .
Technologies
Operating System Windows, UNIX
WORK HISTORY:
Data Analyst
MTU College Of Computing | Houghton, MI May 2023- Dec 2023
Description: Worked under a project associated with Michigan Technological university Conducted a
comprehensive analysis and comparison of three machine learning algorithms logistic regression, random forest
classifier, and decision tree classifier for predicting cardiovascular disease presence in patients.
Create and maintain the department website and coordinate the team with research and development
projects.
Prepare summary reports, data manipulation, and scrutinize the department s data on service support and
funding.
Cleaning and organizing student raw data to ensure accuracy and consistency.
Calculating the outcome Rates, analyzing data to identify trends and patterns, and facilitating data-driven
decision-making.
Analyzing informative and visually appealing dashboards and creating reports for presenting data to
stakeholders using MS Excel, Analytical Skills.
Determine operational objectives by studying business functions, gathering information, and evaluating
output requirements and formats.
Define project requirements by identifying project milestones, phases, and elements.
Identify and reconcile errors in data to ensure accurate business requirements.
Draft and maintain business requirements and align them with functional and technical requirements.
Manage project through status meetings, weekly reports, identifying risks, and tracking issues
Design, develop, and deploy business intelligence applications.
Logistic Regression: Executed logistic regression with hyperparameter tuning using grid search, achieving
an accuracy of 86%, specificity of 94%, and sensitivity of 78%.
Random Forest Classifier: Employed a random forest classifier with 20 trees and grid search for
hyperparameter tuning, yielding an accuracy of 86%, specificity of 97%, and sensitivity of 76%.
Decision Tree Classifier: Utilized a decision tree classifier with grid search cross-validation, resulting in an
accuracy of 77%, specificity of 97%, and sensitivity of 57%.
Monitor project progress by tracking activity, resolving problems, publishing progress reports, and
recommending actions.
Environment: Power BI, MS Excel, SQL, data analytics, Tableau, Data analysis, Machine learning, project
management, MS word, Exploration data analysis, data cleaning, database management and warehousing, Data
Visualization, Jira, Microsoft teams, Data Bricks, python. Jupyter Notebook, Visual Studio, Snowflakes.
Digital Data Analyst
Centene Corporation (Enterprise& Data Science Engineering) Clayton, MO May 2023- Aug 2023
Collaborating with the stakeholders to understand the Data management and visualization Request.
Working with a cross-functional team of data Scientists and engineers to collect data from the respective
departments.
Lead the data management activities including data integration across data sources, data analysis and
interpretations, data cleaning, data manipulation, data mapping and developing data quality reports.
Building Machine Leaning Models (predicting of HbA1c values >8) in Python and analysis of results on the
hospitalization data to determine structure that are- predicted (Hba1c) model to estimate the impact of
intervention and change to reduce errors transmission to date and the number of people.
Build, develop, update, manage and publish dashboards for internal and external audiences using Power-
BI.
Creating data packages and modelling data integration flows with SQL Server Integration Services (SSIS)
from various data sources.
T-SQL is extensively used in constructing user functions, views, indexes, user profiles, relational database
models, data dictionaries, and data integrity.
Extensive experience in Information Technology with special emphasis on design & development of
Database/Data Warehousing applications and Software Development Lifecycle (SDLC).
In-depth knowledge in various RDBMS concepts like Database, Table Normalization, User Defined Data
Types, Views, Indexes, Stored Procedures, User Defined Functions, Triggers, etc.
Experience working with Azure SQL Database Import and Export Service.
Experience in deploying SQL Databases in Azure.
Proficient in working with Transact-SQL DDL and DML.
Used SQL Profiler and Index Tuning Wizard for tracing slow running queries and stored procedures.
Designed and developed Power BI graphical and visualization solutions with business requirement
documents and plans for creating interactive dashboards.
Utilized Power BI (Power View) to create various analytical dashboards that depicts critical KPIs such as
legal case matter, billing hours and case proceedings along with slicers and dicers enabling end-user to make
filters.
Generated ad-hoc reports in Excel Power Pivot and shared them using Power BI to the decision makers for
strategic planning.
Utilized Power Query in Power BI to Pivot and Un-pivot the data model for data cleansing and data
massaging.
Implemented several DAX functions for various fact calculations for efficient data visualization in Power BI.
Utilized Power BI gateway to keep dashboards and reports up to date with on-premise data sources.
Provided guidance and implemented PostgreSQL database solution in AWS.
Proactive and Reactive tuning of databases in every stage of the project.
Evaluated database performance and performed maintenance duties such as tuning, backup, restoration,
and disaster recovery.
Develop reports using complex formulas and query the database to generate different types of ad-hoc reports
using SSRS.
Wrote and executed various MYSQL database queries using Python MySQL connector and MySQL DB
package. Creating the Automation scripts using Python, Configured and trained users to use Reporting
Services. Created parameterized Reports using Charts, Crosstab and Sub-report types.
Develop new reports as needed/requested to provide timely and accurate information to decision-makers.
Environment: SQL Server (SSIS, T-SQL), Azure SQL Database (Import/Export Service, Deployment),
PostgreSQL (AWS)Python (libraries like pandas for data manipulation, scikit-learn for machine learning),
Power BI (DAX functions, Power Query, Power View), Excel (Power Pivot, Power BI, Excel (Charts,
PivotTables) Project Management & Communication: Not explicitly mentioned, but likely project management
software (Jira, Microsoft Teams)
Junior Quality Control Analyst
Chromo Laboratories India pvt. ltd.| Hyderabad, India. Sep 2020- Nov 2021
Applied advanced statistical analysis using tools such as Minitab to conduct investigations and case
studies, ensuring regulatory compliance and adherence to industry standards.
Implemented quality management software to inspect and compare quality control records against
standards, contributing to a streamlined quality assurance process.
Utilized Tableau for in-depth data analysis of quality control records, providing valuable insights into product
quality and safety.
Generated detailed reports using Microsoft Excel and Power BI to communicate analytical findings,
contributing to data-driven decision-making.
Perform daily, weekly and monthly reviews and analyses of current processes using operational metrics and
reports.
Analyze client s business requirements and processes through document analysis, interviews, workshops,
and workflow analysis.
Conduct functional, regression, user acceptance, integration, and performance testing to verify the user s
needs are met.
Communicate business requirements by constructing easy-to-understand data and process models.
Engage the client in gathering software requirements/business rules and ensuring alignment with
development teams.
Environments: Data Analysis & Visualization, Statistical Software: Minitab Data Visualization Tool: Tableau
Spreadsheets: Microsoft Excel
Research Analysis and Prediction of Diabetes using AI.
A machine learning model that can predict the risk stage of diabetes mellitus patients using AI applications.
Used data visualization and data cleaning analysis and machine learning and deep learning tools to collect
and predict the data.
Explored algorithms like Logistic Regression, Random Forest, SVM, or Boost for risk stage prediction.
Utilize features beyond demographics, including blood tests, blood pressure, weight, genetics, diet, and
exercise data.
Aim to predict not just diabetes presence but also the stage for targeted interventions.
The Data Analysis Pipeline includes handling missing data using imputation or removing problematic data
points.
Create data visualizations (scatter plots, box plots) to reveal patterns and relationships between features and
diabetes risk.
Advanced Techniques like Implement Case-Based Reasoning (CBR) with a well-defined case library for
similar patient data and outcomes.
Considered deep neural networks for complex datasets but remember the need for significant data and
computational power.
Correlation analysis, chi-square tests, and hypothesis testing were used to understand feature relationships.
Creating new features from existing ones (e.g., BMI) through feature engineering to improve model
performance. Case-based reasoning (CBR) and artificial neural networks are applied in the diagnosis.
This is a predictive modelling project aimed at predicting whether a patient will show up or not using various
Machine Learning Techniques that also advance EDA (exploratory data analysis).
used Python, R, and SAS imp for cleaning the dataset, KNIME for modelling, and Tableau to create
advanced visuals and dashboards.
Environment: ML, PYTON, Tableau, data visualization, and data cleaning Tools.
Health IT system Analysis on OSF Healthcare & Schoolcraft Memorial Hospital and Practical
Improvements.
Analyzed two different EHR systems (EPIC and CERNER) with two different health systems and potential
improvements in the system and interoperability issues.
Identify Specific Functions by Analyzing and comparing how each system handles functionalities like Patient
demographics and medical history recording, Order entry and medication management, Clinical decision
support tools, Reporting and data analytics capabilities, and Patient portal access and functionality.
Interacted with different healthcare professionals from different hospitals and how organizations advocate
existing HIE programs and needs and improvements of end consumers in Health IT.
Prepared PPT presentation and submitted it to Health care professionals in the Upper peninsula of
Michigan (UPHIE), OSF and Schoolcraft Hospital.
Data Interoperability: Assess how well each system interacts with other systems.
Ability to exchange patient data securely between OSF and Schoolcraft
o Integration with external labs, radiology services, or pharmacies
o Potential challenges due to different data formats or communication protocols
User Interface (UI) and Usability by Comparing the ease of use and intuitiveness of each system's interface.
Considering factors like screen layout, navigation, and information accessibility.
How Health Informatics Can Enhance Digital Health:
Facilitated data-driven decisions that improve clinical outcomes -Streamline and improve clinical workflow
and processes, reduce medical errors associated with manual data entry.
Analyzed to secure exchange of patient information -Improve patient outcomes with evidence-based
practices and care through telemedicine.
Lead through meetings with stakeholders and Health care professionals and weekly meetings, identified
negative consequences including increased mortality rates and strategies to improve the public health by
advocating for enhanced access to healthcare services.
Analyzed Patient data to identify trends in readmission rates for specific conditions (e.g., heart failure).
Developed Recommendations for implementing post-discharge monitoring programs based on data analysis.
Collaborated With healthcare professionals to streamline clinical workflows using data insights (e.g.,
automating medication dosing calculations).
Reduced Medical errors associated with manual data entry through data-driven workflow improvements.
Secure Exchange of Patient Information
Evaluated security measures (encryption protocols, two-factor authentication) to ensure HIPAA compliance
with patient information exchange.
facilitated Improved patient care coordination between specialists at different institutions through secure data
exchange.
Empowered Patients to manage their health information by enabling access to consolidated medical records.
Supported Telemedicine initiatives by ensuring secure exchange of patient information for remote
consultations.
Leadership Through Stakeholder and Healthcare Professional Meetings
Led Meetings with stakeholders (administrators, IT teams, patient advocacy groups) to discuss data-driven
healthcare initiatives.
Advocated For budget allocation for implementing new data analytics software to improve patient outcomes.
Identified Negative public health consequences (e.g., increased mortality rates) linked to lack of access to
preventative care services.
Developed Strategies to improve public health through increased access to healthcare services (e.g., mobile
health clinics, Medicaid expansion).
|
