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SEPHORALead Machine Learning Engineer
Dec. 2024
ShiptSenior Machine Learning Engineer II
Jan. 2022 - Nov. 2024• Led the deployment of a Dockerized Customer Segmentation Model within a managed Kubernetes cluster, orchestrated through Apache Airflow. Implemented MLOps best practices by establishing comprehensive monitoring, alerting, and logging systems, ensuring efficient debugging and operational excellence throughout the model lifecycle. • Led developing and deploying a Dockerized Out of Stock prediction model using LightGBM, with parameters optimized through Bayesian Statistics. Built it as a robust ML batch pipeline, adhering to MLOps best practices for model lifecycle management, ensuring traceability, observability, and seamless integration with analytics dashboards for real-time monitoring.
Levi Strauss & Co.Machine Learning Engineer
Aug. 2020 - Dec. 2021Remote• Implemented Dockerized LSTM Deep Learning Classification Models as REST APIs within a Google Kubernetes Engine Cluster. • Revamped initial model iterations, resolving inconsistencies and low-quality product recommendations (complementary, similar, personalized). Architected LSTM model, integrated Keras-Tuning, Tensorboard, and MLOps principles, resulting in a 2x improvement in metrics. • Implemented Dockerized XGBoost models as Machine Learning Pipelines utilizing Apache Airflow and AWS Batch to address Adjusted Demand and Out-Of-Stock issues within the supply chain. Elevated the process from manual deployments and overrides to a production-ready application through the leadership of an engineering team, resulting in cost savings for the company. • Developed a company-wide Machine Learning Operations Architecture to standardize infrastructure development, model output versioning, feature handling, and integration into a centralized ML model registry supported by MLFlow. This initiative was crucial as our Machine Learning Engineering team expanded, ensuring consistency and efficiency in our processes.
PaylocityData Scientist
Dec. 2019 - Aug. 2020Remote• Created an in-house Python Package to streamline Data Science workflows, including EDA and Configuration Management. I deployed it to our internal PyPi Repository in Artifactory, saving data scientists significant time that would have been spent building similar tools. Embracing the Open-Close Principle, I designed the package for extensibility. • Oversaw the infrastructure for the Employee Retention Model, involving Apache Spark processing through the Python Dataframe API. Developed Light Gradient Boosting Models within Apache Spark MLLib and deployed them on a Databricks Cluster hosted on Azure Cloud. My adjustments to the probability threshold led to a 1.5x improvement in our metrics. • Created and sustained the REP Score Measurement system, incorporating Functional Object-Oriented Programming (FOOP) principles to streamline software execution and accelerate development processes.
NielsenData Scientist
Dec. 2017 - Dec. 2019Remote• Led migrating a large-scale SAS application to AWS Cloud Python using Data Science-specific SDLC concepts. It included unit testing, DRY (Don't Repeat Yourself) and KISS (Keep It Simple, Stupid) principles, a Jenkins Pipeline for continuous integration, and Docker containerization. This transfer demonstrated my technical skills and ability to combine data science with software engineering. • Continuously enhanced the Total Media Fusion system by implementing parallelism, resulting in remarkable speed improvements of up to 60x. Introduced JIT-compiled functions for efficient Linear Algebra operations, including Cholesky Decomposition and Euclidean Measurement for Mananoblis Distance calculations. Additionally, played a pivotal role in optimizing QA processes. • Designed a Look-A-Like Model to refine customer segmentation, leveraging a machine learning-driven approach over traditional analytics. • Managed, developed, and streamlined a Probabilistic Advertisement Model for predicting ad visibility, transitioning it from a VBA-based process to a pure Python solution.
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