• Reduced end-to-end testing time by 66% by implementing an SSH tunnel with Paramiko to execute the security process on-demand, eliminating the need to wait for the nightly scheduled run

• Improved overall system performance by 40% post-server migration by conducting performance tests using JMeter and resolving an error with authentication

• Achieved a 45% reduction in troubleshooting time by configuring and deploying the Elastic Stack to centralize, monitor, and analyze log data

• Automated deployment, configuration, and management tasks across 100+ Linux servers by designing Ansible playbooks and leveraging Ansible Tower

• Improved response time to performance issues by 60% by developing a Python script that monitors health data from the Solr API and automates alerting

• Resolved thousands of data replication discrepancies between Solr and Db2 by developing a Python process to delete extraneous Solr records and reindex missing data

• Increased overall code coverage by 10% by expanding ingestion and API tests to include edge cases

• Enabled continuous code validation and early bug detection by integrating testing into the CI/CD pipeline

• Automated building, testing, and deploying containerized applications by building a DevOps pipeline with Jenkins, Ansible, Docker, and Kubernetes

• Utilized UFT (Unified Functional Testing) to develop and execute automated test scripts, ensuring comprehensive test coverage.

• Achieved over 90% accuracy in forecasting COVID-19 outbreaks in nursing homes by developing a Python-based predictive model using scikit-learn

• Reduced processing time by 93% and ensured strict HIPAA compliance by leveraging Python and scispaCy to redact sensitive information from over 100,000 medical records

• Built a search tool that enabled researchers to analyze 50,000+ journal articles on COVID-19 efficiently using Python, NLTK, and Gensim

• Improved HR workflow efficiency by 30% by utilizing Python and spaCy to extract employee data from resumes and proposals.

• Created high-quality data visualizations and dashboards for client presentations and reports using Python and Tableau.

• Achieved a 75% reduction in geocoding time by spearheading the development of a batch geocoding tool using Python, Google Geocoding API, and Bing Maps API.

• Led teams of 3-6 engineers in developing, testing, and deploying data collection applications, ensuring timely and accurate project delivery

• Managed two concurrent large-scale transportation projects with a combined budget of $8 million, ensuring projects were completed on time and within budget

• Automated several ETL processes using Python to improve the data quality of survey data, resulting in a 20% reduction in overall processing time.

• Regularly met with clients to assess data needs, provide project updates, and build reputational capital.

• Coordinated with project directors to determine project budget and forecast labor projections.

• Managed all stages of projects including proposal development, kick-off, implementation, data management, analysis, and reporting.

• Maintained and updated survey databases by conducting quality checks and data cleaning.

• Supervised in-person field staff and managed the survey data collection effort.