Shiva Raju G K

MS in Bioinformatics

August 2021 - May 2023

Relevant Coursework

• Biomedical Analytics
• Database Management
• Visualization
• Machine Learning
• Deep Learning Neural Networks

B.E. in Biomedical Engineering

Sep 2013 - June 2017

Relevant Coursework

• Introduction to Biomedical Informatics
• Healthcare Data Management
• Electronic Health Records
• Biomedical Image Processing
• Human biology and physiology courses

State of Florida - Department of Health

July 2023 - Present

Research Consultant

Conduct epidemiologic and machine learning-driven analyses to improve public health insights, focusing on maternal and child health (MCH), Medicaid data analysis, and uterine fibroid research. Lead data science initiatives, develop statistical models, and create interactive dashboards to facilitate decision-making. Manage grant-related activities, mentor interns, and collaborate with cross-functional teams to integrate data-driven solutions into public health strategies.

• Collaborated with clinical and epidemiologic teams to harmonize data structures across REDCap, EPIC, and Medicaid claims, facilitating semantic mapping of patient data for integration into analytical frameworks.
• Automated data pipelines using SAS Macros and parameterized SQL queries to streamline retrieval, validation, and standardization of Medicaid claims and surveillance datasets.
• Developed an electronic relational database to track uterine fibroid–related data from hospital admissions, insurance claims, and clinical records, supporting unified data access.
• Designed and administered REDCap surveys and leveraged SAS for data modeling, contributing to structured research data collection and compliance.
• Conducted epidemiologic analyses on chronic disease and MCH using PRAMS and BRFSS, developing R Shiny dashboards for monitoring trends.
• Implemented machine learning—including NLP and transfer learning—to extract and classify uterine fibroid symptoms from free-text patient complaints, improving model performance.
• Developed Bayesian models and time-series forecasting for infectious disease trend prediction.
• Conducted geospatial risk assessments using ArcGIS Pro to identify disease hotspots and vulnerable populations, and to guide interventions.
• Designed interactive dashboards using Tableau and Visme to support real-time decision-making by public health stakeholders.
• Designed MCH data dashboards using Visme, enhancing accessibility, and mentored interns in data analysis and visualization.
• Developed customized statistical reports and visualizations for health department officials, improving interpretability of analytic outputs.
• Conducted Medicaid data analysis using CPT and CDT codes to identify fraud, and developed monthly reports on enrollment and expenditures.
• Managed epidemiological report generation to leadership, ensuring timely disease trend updates.
• Designed sampling strategies and experimental study designs for public health surveillance.
• Represented the MCH data analysis team in Florida’s Maternal Mortality Review Committee meetings since Q3 2024.
• Worked on a quality improvement project on ER-reported pregnancy cases with syphilis in Miami-Dade County.
• Coordinated grants, ensuring timely deliverables and annual applications.
• Developed scientific writing products, including reports, fact sheets, and conference presentations.

YanLab, IUPUI

September 2021 - May 2023

Research Assistant

Conduct advanced data analysis on large-scale longitudinal demographic and clinical datasets, applying machine learning, statistical modeling, and data engineering techniques to extract meaningful insights for disease epidemiology and healthcare research. Develop automated data pipelines, optimize code efficiency, and create custom data visualizations to support data-driven decision-making.

• Conducted longitudinal data analysis on demographic and clinical data from UK Biobank and ADNI, utilizing Python and R for data preprocessing, feature engineering, and statistical analysis.
• Implemented machine learning algorithms, including random forest and logistic regression, to predict disease progression.
• Utilized Python, R, and Bioconductor packages for genomic data analysis, clustering, and differential gene expression studies.
• Conducted bioinformatics-driven statistical analysis on genomic and biological datasets, supporting next-generation sequencing (NGS) research.
• Developed and automated data pipelines using Bash scripting for ETL processes, handling demographic and ICD-10 data.
• Ensured data accuracy and consistency, reducing data processing time by 40%, enabling timely disease epidemiology and healthcare utilization analysis.
• Applied data mining techniques on EHR using SAS 9.4 to identify patterns and trends in patient outcomes and treatment effectiveness for chronic neurological conditions.
• Assisted in developing, maintaining, and collecting structured and unstructured datasets for analysis and reporting.
• Optimized SAS, Python, and R scripts, making them generic and reusable, reducing code complexity by 20%.
• Created custom data visualizations and interactive dashboards to communicate complex data insights to stakeholders, improving decision-making and strategic planning.
• Developed a static website using Jekyll framework, increasing the lab’s online presence and fostering collaboration.

Accenture Pvt. Ltd.

October 2017 - August 2021

Data Analyst

Utilize statistical modeling, data engineering, and automation techniques to optimize health insurance analytics, data quality monitoring, and business intelligence solutions. Develop and maintain automated data pipelines, enhance database performance, and create interactive dashboards to drive data-driven decision-making in the healthcare domain.

• Analyzed product data and developed statistical models to identify trends and patterns in health insurance outcomes, improving predictive accuracy.
• Designed test strategies aligned with healthcare business requirements, leading to a 30% reduction in testing time.
• Implemented SAS macros to automate ad-hoc data manipulations, imports, exports, and updates, increasing data accessibility by 25%.
• Developed real-time monitoring dashboards for healthcare data pipelines, enabling proactive identification and resolution of data quality issues and performance bottlenecks.
• Optimized database performance and query efficiency by fine-tuning MySQL queries, implementing specialized indexing strategies, and leveraging database tuning techniques for large-scale healthcare data management.
• Developed and maintained 100+ Java and Python scripts to automate data extraction, transformation, and loading (ETL) processes, resulting in a 40% increase in efficiency.
• Integrated automated tests into Jenkins pipelines, ensuring continuous test execution with each code commit.
• Configured Jenkins jobs to report test results directly into Jira, allowing for centralized tracking of test outcomes within the project management system.
• Collaborated with healthcare stakeholders to understand reporting needs and translated them into interactive Tableau dashboards, providing intuitive visualizations of critical healthcare metrics and KPIs for data-driven decision-making.

Chakra IT Solutions

September 2018 - February 2019

SAS Programmer Analyst

Provide SAS programming and statistical analysis expertise for Phase I-IV clinical trials in Oncology and CNS, ensuring data integrity, regulatory compliance, and efficient reporting. Develop standardized datasets, programming macros, and clinical trial outputs aligned with CDISC standards and study requirements.

• Provided SAS programming and analytical expertise for Phase I-IV clinical trials in Oncology and CNS.
• Developed and validated SDTM/ADaM datasets per CDISC standards, including DM, LB, DS, VS, EX, SUPPDM, ADSL, and ADAE.
• Leveraged aCRF, Mapping Specs, IG, Controlled Terminology, and Pinnacle21 validator to uphold data quality and regulatory compliance.
• Produced Tables, Listings, and Figures (TLFs) in alignment with study specifications and mock shells.
• Manipulated SAS datasets using MERGE, SORT, MEANS, FREQ, REPORT, SQL, and TRANSPOSE, generating outputs via ODS for clinical trial reporting.
• Developed efficient SAS macros, %LET variables, CALL SYMPUT functions, and DATA NULL techniques to streamline clinical data cleaning, validation analysis, and report generation.

Languages and Databases

Frameworks

Visualization

Tools and Technologies