Philip Ramirez, Ph.D. Email & Phone Number

Vancouver, Wa, Us

Hoboken, New Jersey, Us

Hoboken, New Jersey, Us

• Evaluated available Big Data technologies and architected a scalable, governed, robust Lakehouse solution (AWS/Databricks) for enterprise healthcare data• Created the blueprint for how to own, plan, and drive the engineering implementation of data solutions involving cross-functional inputs and priorities• Spearheaded the data integration and modeling initiative, transitioning Vytalize Health from a monolithic data solution to a malleable big data platform guiding by best ETL/ELT principles • Develop, maintain, and safeguard technology standards and implementation best practices • Mentor and provide technological guidance for mid level engineers and analysts

• Joined team of data engineers in a mentorship/leadership capacity• Leveraging DevOps experience, transitioned existing pipelines to containerized, cloud based environment• Refactored, optimized, and automated (with CI/CD principles) adhoc onboarding pipelines in order to scale• Used Big Data principles and technologies toward heathcare data, gleaning nationwide insights

Cupertino, California, Us

• Continuously monitored and applied necessary adhoc fixes to machine learning NLP model pipelines• Built internal tools to increase the agility of model promotion from training to device deployment • Developed full-stack web app with Python (deployed with Docker and Kubernetes) to track and visualize pipeline/model performance metrics, i.e., model staleness and model error rates over time• Consistently synced with engineers, data scientists, and analysts on dynamic project parameters

• Developed scripts to assist on model training and model deployment at scale• Architected and maintained database systems and pipelines across multiple teams• Built a full stack web tool aimed at generating training/test data utilizing a Spring Framework• Wrangled, processed, and transformed data leveraged by NLP Machine Learning Models• Automated Grammar data generation and evaluation process used by the Computational Linguists• Parsed, analyzed, and visualized archived data to gain insights into usage patterns• Applied a K-Means clustering algorithm to unlabeled data to identify highly requested new features

Cupertino, California, Us

• Tested, diagnosed, fixed, and delegated Siri natural language bugs accordingly• Tooled with Python, using internal APIs, to automate tasks related to machine learned language• Queried big data in order to compile, process, and transform data for analysis• Created the infrastructure for new natural language domains in order to support future Sirifunctionalities• Defined and maintained documentation guidelines detailing language taxonomy• Continuously synced with engineers, managers, and analysts in order to sharpen the accuracy of Siri's machine learned models

Cupertino, California, Us

• Enhanced the functionality of cloud localization services through data analysis, interpretation, and voice command quality assurance • Collaborated with Project Engineers and team members in order to sync on dynamic project parameters • Upheld high level productivity and accuracy performance metrics while adapting to constantly changing guidelines • Performed weekly evaluations highlighting peers’ strengths and deficiencies with training data

Phd Economics

Bachelor’S Degree, Economics

Master’S Degree, Economics