Senior Analyst I Data Science
CurrentDeveloped containers and orchestrated their deployment in a secure environment using Docker Compose yaml and Dockerfiles.Created DAGS in Airflow for the batch processing of the XML documents.Developed templates using Jinja to batch process about 700k XSL-FO documentsUsed prompt engineering with Github Copilot to convert IBM Datastage jobs to Databricks and PySparkEngineered prompts to in Github Copilot to create unit tests based on code changes in git reposCreated ML models to predict mass closure incidents in a ServiceNow implementation using GCP BigQueryDeployed ML models to endpoints using Vertex AIDeveloped analysis of ITSM data to identify incident cascades initiated by other incidents within ServiceNowDeveloped scripts in python using Pandas to retrieve and deduplicate Nagios state change data at 5 minute intervals on a RHEL VMCreated models to predict the duplication of ServiceNow incidentsConducted analysis of ITSM data to optimize a ServiceNow implementationDeveloped a seminar on using Databricks and PySpark to analyze Energy Information Agency (EIA) electricity production dataBuilt data pipelines in Python to scrape EIA electricity production data and SEC log files into cloud storageImplemented and modified Terraform scripts and Jenkins to automate AWS deployments.Wrote and refactored SQL and bash shell scripts to transition a client from Cloudera to AWS EMR service using Hive.Developed a pilot model using a Ridge Classifier (L2 regularized logistic regression) and Random Forest from Scikit-Learn, and reporting in PowerBI to determine the root cause of patching failures in Windows 10 for a major client.Working on a remote AWS EC2 instance and Jupyter Lab, designed a metric for analyzing the technical viability (based on technical debt as measured by software end of life) and value (based on cost per server) of updating and migrating top 200 clients to cloud and other services.