Srineet

Resume

Srineet Srigiri
Ph: (857) 437-9521 | srigiri.s@northeastern.edu | LinkedIn

Northeastern University, Boston, MA (Sep 2021 - April 2023)
Master of Science in Health Informatics (GPA – 3.78)
Geethanjali College of Pharmacy (Aug 2015 - May 2019)
Bachelor of Pharmaceutical Sciences (GPA – 3.65)

Programming: Python, R, SAS, VBA
Databases: MySQL, PostgreSQL, MS SQL Server, MongoDB, MS Access
Analytics: Advanced Excel, R Studio, Tidyverse, SQLite, Pandas, NumPy, ProcSQL, SASMacro
Visualization: Tableau, Microsoft Power BI, Quick Sight, Spotfire, Lucidchart, Plotly, Matplotlib, R-ggplot2, Visual Studio
Clinical Standards: ICD-10, SNOMED-CT, HIPAA, HL7, FHIR, HEDIS, LOINC, EDI, EPIC, Cerner

Maintained health information systems and databases, including quality scorecards, PDMP, and MES reporting, ensuring high-quality data collection and reporting
Collaborated with internal teams using JIRA to track project progress and implement informatics solutions
Designed and optimized ADT encounter, laboratory, and service utilization reports for major payers (BCBS, UHC, NTC, Molina), ensuring accurate, actionable analytics
Led end-to-end AWS Glue DataBrew workflows to profile, cleanse, and transform PostgreSQL, Redshift, and S3 CSVs, integrated into QuickSight, reducing data-tracking time for leadership and managers
Engineered complex Amazon Redshift SQL queries to extract and analyze Nebraska and Iowa HIE datasets supporting population-health cohorts, DHHS and academic collaboration reports—leveraging HL7 and clinical terminology standards, while enforcing HIPAA/PHI-PII safeguards and HITRUST-secured file transfers
Engineered a custom Python HL7 parser to process legacy files missed by the integration engine, enabling comprehensive payer reporting and KPI calculations
Designed AWS Lambda functions to automate empty-file alerts and S3 bucket transfers, cutting manual processing time by 50–60%
Leveraged SDOH/HRSN data to build detailed encounter reports for referred populations, enabling care teams to identify and address social needs

Developed and executed advanced SAS scripts to establish ODBC connections to DB2 libraries, efficiently retrieving provider, patient, and claims data through optimized ProcSQL queries
Enhanced script efficiency by leveraging SASMacro for automation and parameterization, eliminating code redundancy and accelerating script execution time by approximately one hour per script
Streamlined and automated over 2500 excel PCMH and ACO quality reports for all affiliated providers using VBA Macros, achieving a reduction of 20 hours in monthly handling time
Constructed dynamic Tableau scorecard dashboards for providers, facilitating real-time insights by integrating .xlsx files with Tableau Server using customized SAS scripts
Created multiple ad-hoc reports to program directors based on their requirements using MS Access, ProcSQL, and Excel/VBA

Assisted Professor Jimison Holly in collaboration with MassAITC in the Stakeholder Analysis project for Alzheimer's care, including background studies, interviews, and qualitative data analysis
Supervised and taught the lab for 250 students in the course “Computer Science and its Applications (Advanced Excel)”, covering LAMBDA and LOOKUP functions, visualizations, and array functions

Explored Takeda’s Cis-Regulator Elements database with around 2.3 billion elements and identified the number of overlapping elements within the whole cohort of the CREdb, using R, SQL, and Amazon Athena
Implemented JDBC connection to Amazon Athena using R packages (rJava, RJDBC), executed queries to fetch data from the database, and utilized AWS S3 as a staging directory for data retrieval
Developed a prototype CREdb Interface application using R-Shiny and SQL to visualize information about elements based on user selection

Automated Excel sheets using macros/VBA to reduce average handled time by ~300 hours through daily data entry
Promoted as a Subject Matter Expert and trained two batches of 10 associates in the business process
Created advanced QuickSight dashboards for Amazon Transportation Services, tracking daily loads coming to Amazon facilities

Designed and executed a machine learning project in R focused on analyzing mental health in the tech industry, utilizing models such as Logistic Regression, K-Nearest Neighbor, Decision Trees, and Support Vector Machine
Employed various evaluation techniques, hyperparameter tuning, and ensemble methods to optimize the model's accuracy, achieving approximately 86% accuracy in predicting whether an employee should seek professional help

Developed a personalized machine learning algorithm on Jupyter Notebook using raw accelerometer data from a smartwatch to accurately calculate total sleep duration, sleep onset, and end times
Achieved a high accuracy rate of 96% when compared to smartphone data, validating the effectiveness of the algorithm

Analyzed large Medicare Provider Utilization and Payment Data Claims using R and MS SQL to uncover disparities across physicians and state life expectancies of Medicare patients, utilizing ICD-10 codes while adhering to HIPAA privacy standards

Developed and optimized a MySQL Workbench database for a food delivery system, creating ER diagrams, establishing relationships, and forward engineering, achieving 100% CRUD operation accuracy and improving query response time by 6%

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