Thomas Corcoran
ML EngineerML @ Citizen Health
Citizen Health
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
ML @ Citizen Health
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Driving healthcare innovation at Citizen Health to empower patients and enhance data-driven decisions.
Udacity
Nanodegree, Machine Learning Engineer
January 1, 2016 – Present
University of California, Santa Cruz
Bachelor of Arts (BA), Linguistics
January 1, 2010 – June 1, 2014
Citizen Health
Machine Learning Lead
December 1, 2023 – Present
San Mateo, California, United States · Hybrid
Invitae
Machine Learning Engineer
September 1, 2021 – December 1, 2023
Remote
Ciitizen (acquired by Invitae)
Data Scientist / Machine Learning Engineer
July 1, 2020 – September 1, 2021
Palo Alto, California, United States
Change Healthcare
Data Scientist
November 1, 2018 – July 1, 2020
Emeryville, CA
Automation Anywhere
Lead Machine Learning Engineer
November 1, 2016 – November 1, 2018
San Jose, California
Gofind.ai
Natural Language Processing Engineer
May 1, 2016 – October 1, 2016
Berkeley, CA
Yowlumni Language Project
Developer
December 1, 2015 – May 1, 2016
Good Earth Growers
Technology Consultant
November 1, 2014 – May 1, 2016
Natural Language and Dialogue Systems Lab Baskin School of Engineering, UC Santa Cruz
Research Intern
June 1, 2013 – January 1, 2014
Linguistics Department, UC Santa Cruz
Student Researcher
May 1, 2012 – December 1, 2012
Santa Cruz. CA
Wikipedia Author Identification
April 1, 2016 – Present
• Created a corpus of Wikipedia talk pages • Implemented a convolutional neural network in TensorFlow to identify authors of talk page posts • Trained model on large-scale cluster achieving 94% accuracy on a test set
Automatic Sarcasm Detection
October 1, 2014 – Present
• Helped to originate novel methods to detect written sarcasm on Internet forums • Co-authored paper that was published in Knowledge-Based Systems • Created linguistically-motivated feature sets using CoreNLP and Python • Trained models on large corpora using scikit-learn and SVM light
Irish Dialect Classifier
March 1, 2014 – Present
• Developed a system to classify dialects of Irish based on a corpus of written Irish • Generated linguistically-motivated features • Trained a machine learning model to correctly classify documents into their respective dialect with up to 90% precision / recall
Narrative Structure Detection
November 1, 2013 – Present
• Co-author on four peer-reviewed published papers • Trained machine learning models to detect narrative structure in fables and personal stories
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an ML Engineer role, particularly one focused on NLP or document intelligence, given their extensive experience in these areas. Their background includes both academic research and industry application, showing a blend of theoretical understanding and practical implementation. The diversity of projects, from sarcasm detection to medical record processing, indicates a broad interest and ability to adapt to different problem domains. The progression into lead roles suggests ambition and a desire for impact. The lack of explicit company culture alignment details prevents a deeper assessment.
Soft Skills & Operational Fit
The candidate's resume indicates strong problem-solving skills through their research and project work, particularly in originating novel methods for sarcasm detection and narrative structure. Leadership roles suggest good team collaboration and project management capabilities. The diverse project portfolio implies adaptability and a proactive approach to learning and applying new technologies. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and direct team collaboration style is not possible.