2019-2021:

* Lead the development of the onboarding educational program for Machine Learning, going from a basically non-existent experience to one that helps developers acclimate to the otherwise overwhelming array of ML tooling/standards available at Spotify. Managed user interviews/feedback to iterate and improve

* Founding member of the team responsible for making TFX/Kubeflow available at Spotify

* Development, deployment, and maintenance of our internal managed Kubeflow cluster on k8s

* Development on our internal python libraries for using TFX connecting w/ other internal tooling.

* One of the core writers of our internal "Golden Path" documentation that almost all ML developers use to set up pipelines. Referred to as "some of the best documentation I've ever seen at Spotify"

* Lead the initial development of SLOs for our kubeflow platform

* Due to the lack of PM, often took on responsibilities such as gathering requirements from users, conducting and consolidating user interviews to improve the developer experience

* On call rotations for our k8s infrastructure

* Regular week long rotations on our customer support services (debugging, helping find solutions within TF/TFX for customer ideas, triaging requests, etc)

2021-Present:

* Working as an internal consultant representative for the ML Platform

* Helping teams with various ML needs such as onboarding onto platform tooling through short-term team embeddings: migrating legacy pipelines, adopting new tools, teaching best practices, developing advanced functionalities not yet available in platform tooling, etc. Lots of exposure to diverse ML problems throughout the whole company.

* Providing and consolidating feedback from customer teams to the ML Platform to help inform and drive new initiatives across the whole ML platform to increase ML developer productivity across the entire company

2014-2015:

* Assist in building data infrastructure

* Work on ETL pipelines

* Developed open source workflow management software Dagobah

* Tech including Vertica, hadoop, S3, Exact Target, Pig, with my main development language being Python and occasional Java.

* Worked on large data aggregations for ExactTarget/Salesforce marketing cloud

2016:

* Infrastructure stabilized

* Legacy ETL maintenance

* Shifting towards data science

* Churn prediction

2017:

* Additional churn prediction modeling and cost impact reports

* Conversion prediction modeling

* User segmentation exploratory analysis

* Technologies: Sklearn/pandas/numpy, spark/sparkSQL, scala/scalding, zeppelin

* Helped search team improve search result relevance

2018:

* As of late 2017 we started the Applied Machine Learning team at Vimeo, focused on deploying machine learning models and technologies to production to impact various problems at Vimeo

* Fraud Detection

* Visual classification

* Dockerized classification platform

* Deployment to Google Cloud Platform

* Using GCP technologies such as GKE, Pub/Sub, Datastore

* POC for a hardware embedded realtime object detection using Convolutional Neural Networks (CNN)

* Research for marketing attribution models

* Modeled, architected, and deployed a visual search API for Vimeos new stock video service, utilizing GKE, keras, flask, gunicorn, and ANNOY, handling production traffic >1000 TPS

* Automated and deployed pipeline for visual search and visual tagging for new uploads in near-real time

I worked primarily on the back end, dealing with data sourcing, ingestion, cleaning, and processing (ETL). Mostly using Java, Hadoop, Pig, Hbase, some experience with Amazon AWS EMR. Experience ingesting data via various web APIs, in distributed contexts. Side project involving social network analysis for artists.

6 months full time as a Co-op/paid intern, the rest working part time remotely during school. Working on Machine learning data mining research.

ML topics worked on: Entity disambiguation, genetic algorithms, LDA, TF-IDF, K-means clustering, biologically inspired algorithms research.

Maintained RITs networking and account management software CLAWS (part time job while attending RIT)