David Monk
Principal, Senior Data Analyst
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolDavid Monk is a Principal Senior Data Analyst with 12 years of experience building end-to-end machine learning and computer vision systems, currently leading data work at Sensor Collective from Austin. He blends practical expertise in Python, R, OpenCV, Keras/TensorFlow and PyTorch with cloud and big-data platforms (GCP, Spark, occasional Hadoop/Azure) to deliver product-focused analytics, secure data pipelines, and embedded/edge solutions using MQTT and Raspberry Pi. His background spans finance, service quality, and process mining, and he has a track record of turning public civic and sensor datasets into actionable product insights and prototypes. A former physics researcher and instructor with an M.S. in Physics, he brings strong quantitative modeling and signal-processing chops—skills that surfaced early in sonar simulation and spatial computing projects. Able to work without visa sponsorship across major Western markets, he pairs academic rigor with hands-on product discovery and security-minded deployment.
12 years of coding experience
5 years of employment as a software developer
Master of Science, Physics, Master of Science, Physics at University of WashingtonCourseraUdacityB.S., Math and Philosophy, B.S., Math and Philosophy at Washington State UniversityPreparatory Courses, Engineering Physics/Applied Physics, Preparatory Courses, Engineering Physics/Applied Physics at Seattle Central Community CollegePhysics, Electrical Engineering, Chemistry, Physics, Electrical Engineering, Chemistry at Seattle Central CollegeGarfield high School
English, Spanish, French, 中文 （普通话）
Github Skills (8)
smartphone9
gadget9
android-application9
closed8
android-applications8
pebble7
liveview6
android5
Programming languages (1)
Github contributions (5)
velowander/human-activity-recognition
May 2014 - Jun 2014
Contributions:19 commits in 21 days
blueboxio/Autopilot-TensorFlow
Sep 2018 - Oct 2018
A TensorFlow implementation of this Nvidia paper: https://arxiv.org/pdf/1604.07316.pdf with some changes
Contributions:19 pushes, 2 branches in 28 days
arxivpdfnvidiadeep-learningmachine-learning
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