resume.txt

Brent Schneeman
ENGiNEERiNG AND DATA SCiENCE
1903 Barton Parkway, Austin TX 78704
 512.298.8880 |  schneeman@gmail.com |  schnee
|  schneeman
Overview
AI and machine learning executive building teams, products, processes, and strategies to deploy algorithmic models
in real‑time customer facing applications.
Broad leadership experience across data scientists, software engineers, data analysts, and data engineers, having
scaled‑from‑zero durable teams across disciplines and companies. Mixes software engineering, machine learning,
and leadership skills to focus on rapid learning and hypothesis testing. Adept at combining engineering and science
to help companies both unlock the value of data and create new products as they pivot to modern algorithmic‑
based business models. Led and implemented data solutions to provide APIs and platforms which expose ML and
AI to stakeholders and bring strong hands‑on experience in Artificial Intelligence, machine learning projects, natural
language processing (NLP) and computer vision (CV).
Active in local meetups and conferences, presenting on a variety of Data Science and Machine Learning topics, as
well as being an Advisory Board Member for UT‑Austin, McCombs School of Business Masters of Science Business
Analytics program since 2015.
Expertise
ORGANiZATiONAL
Collaboration
Miscellaneous
Project
Management
Strategic thinking, Engineering and Science leadership, C‑Level adviser, Team design
Operationalizing algorithms, Product orientation, Academic liaison, Curricula design
Agile, Scrum, Kanban, Deterministic and Probabilistic
Experience
Overhaul, Inc
ViCE PRESiDENT, DATA SCiENCE AND ANALYTiCS
June 2020 ‑ August 2022
• First hire with data‑specific focus, built Data organization with Data Engineering, Data Analytics, Data Science and Machine Learning focus.
Guided by collaboration with stakeholders, built team to deliver internal and external data‑as‑a‑product features (e.g. reports, data assets).
Provided direction for machine learning and data transformation pipelines, and for a robust data warehouse strategy. Achieved massive au‑
tomation of data delivery to end customers (thereby realizing operational inefficiencies). ML efforts included transportation duration models,
scoring of security events, and agent‑based modeling of backhaul opportunities
• Six‑month international assignment in 2022 to build Data Science and Engineering team in Ireland. Collaborated with local recruiters, groups
and universities to build recruitment pipeline and better understand European hiring processes and talent. Resulted in data science and ma‑
chine learning hires in‑country
• Technical architect, team lead, and main SQL contributor to Prefect‑ and DBT‑based data pipeline to populate Snowflake data warehouse.
Ensured predictability, explainability, and observability to analytic data
• Hired 8‑person international team of Business Intelligence Developers, Data Scientists, Data Engineers, and Machine Learning Engineers
JUNE 2023
BRENT SCHNEEMAN · CURRiCULUM ViTAE
1Alegion, Inc
SENiOR DiRECTOR, MACHiNE LEARNiNG
March 2019 ‑ April 2020
• Ideated, proposed, and implemented Active Learning workflow coupling machine learning with human annotators, which provides significant
workflow efficiency. Delivered Sagemaker‑based solution in 2019 for image classification and object detection; delivered Kubeflow and DVC
based solution in 2020, focusing on computer vision and natural language processing capabilities
• Identified new product opportunities and developed new SKUs for Sales team
• Led machine learning engineering and architecture team responsible for creating human‑in‑the‑loop machine learning capabilities into the
core product
• Assume roles of engineering leader, scrum master, product owner, and team member as appropriate; skilled at blending Machine Learning and
Software Engineering process techniques to deliver ML‑based solutions
• Implemented and directed Alegion’s intellectual property program by liaising between outside Counsel, internal inventors, and executive spon‑
sors
• Active in local meetups, spoke at Data Day Texas in January 2020
• Delivered Computer Vision solution using AWS Sagemaker and Faster R‑CNN‑based for object classification and detection for customer‑specific
video and image use cases. ML improved throughput by 30% over the fully‑manual process, and uses AWS Lambda functions and SQS mes‑
saging
• Led efforts for cross‑frame entity resolution of objects in videos. Then‑current heuristic approach determined ‘good enough‘ relative to ML/AI
approaches. Team deployed to higher ROI efforts
• Delivered Kubeflow, Kubernetes, Weights and Biases‑based metrics, and DVC‑based ML infrastructure, focusing on computer vision and natural
language processing capabilities. This provides alternatives to the Sagemaker efforts, and gives greater model composition, reproducibility and
data tracking. Target use cases for computer vision and NLP
• Personally built, trained and deployed color‑classification CNN‑based model for retail fashion catalog. Leveraged aforementioned ML infras‑
tructure. Model allows Alegion to by‑pass Crowd for certain color classes, greatly increasing margin
• Architected and led delivery of Active Learning workflow coupling machine learning with human annotators, which provides significant workflow
efficiency, chiefly for image annotation tasks. System deployed on Kubernetes‑based infrastructure hosted on Amazon Web Services (AWS)
• Identified new product opportunities and developed new SKUs for efficient sales penetration, based on real‑world machine learning engineer‑
ing processes and rapid hypothesis testing
• Collaborate with VP Sales and CEO to provide high‑touch contract negotiations with key customers. Provide superlative support for Sales in
front of these customers
• Built and led machine learning engineering and architecture team responsible for creating human‑in‑the‑loop machine learning capabilities
into the core product
• Assumed roles of engineering leader, scrum master, product owner, and team member as appropriate; skilled at blending Machine Learning
and Software Engineering process techniques to deliver ML‑based solutions
• Implemented and directed Alegion’s intellectual property program by liaising between outside Counsel, internal inventors, and executive spon‑
sors
• Active in local meetups, spoke at Data Day Texas in January 2020
• Reporting to Vice President, Engineering; dotted line to CTO
UT McCombs School of Business
INDUSTRY ADViSER
2015 ‑
• Advising McCombs on current industry needs for the Masters of Science Business Analytics curricula, including Database, Data engineering and
ML modeling concepts. Ran three capstone projects. Hired three graduates and was influential in the hiring of four more.
DOSH, LLC
SENiOR DiRECTOR, DATA SCiENCE
Jan 2018 ‑ Feb 2019
• Built a 9‑person diverse team of Data Scientists, Data Analysts, and Data Engineers, all focused on evolving Dosh to a more data‑ and algorithmic‑
oriented mobile‑app company. The Data and Analytics team was responsible for Data Science, Business Intelligence and Data Engineering (e.g.
building the data warehouse and data lake)
• Advised C‑level business partners to generate marketing campaigns, financial models, sales models, and investor‑deck content
• Collaborated with technical stakeholders to create a data warehouse from Node.js‑based application data and to create ETL pipelines feeding
location data to AWS S3 buckets for graph analysis. Eliminated redundant RDBMS systems in‑favor of AWS Redshift via ETL system build on top
of Matillion
• Sole contributor to XGBoost‑based models predicting offer redemption (transactions) and deeply analyzed fraudulent behaviors and referral
networks using R and various network analytics packages
• Led transformation of company dashboards from ‘vanity‑metrics’ to business‑focused KPIs
• Directed Data Science efforts in predicting causal impact of multi‑vendor markets and price elasticity
• Reported to Sr Vice President, Engineering
JUNE 2023
BRENT SCHNEEMAN · CURRiCULUM ViTAE
2HomeAway, an Expedia Company
SENiOR DiRECTOR, DATA SCiENCE
Jul 2014 ‑ Dec 2017
• Manage, Bootstrap, and Provide Strategic Vision for data science initiatives, such as Inventory Ranking and Optimization, Content Recommen‑
dations, Marketplace Health, Image Quality, Chat bot AI Services, and other business critical efforts
• Strengthened Algorithmic and Machine Learning muscle through project‑based leadership. Partnering with disparate product lines to answer
questions quicker, and find new questions faster. Evangelized data‑informed decision making across company and with key executives, leading
to the formation of the HomeAway Data Science Team. Built and directed 25 person team consisting of PhD Data Scientists and Machine
Learning Engineers
• Team projects included competitive intelligence, fraud detection, bot detection, unstructured and semi‑structured data mining, search result
ranking, photo analysis, and price and occupancy predictions
• Partnered with the UT McCombs School of Business, providing hiring‑manager guidance and data for the Masters of Science Business Analytics
program. Hired 3 graduates from the program (see Volunteering section below)
• Individual contributor on ML modeling for consumer facing chat bot. Duties included labeling data, building Deep Learning‑based intent clas‑
sifier (in Keras), collaborating with data and software engineering to determine application architecture
• Created experience‑based recommendation service that found activities in one location that are similar to activities in another. A word2vec‑
based latent embedding space was used to find the similar activities
• Created photo‑similarity service that allowed clients to explore images of properties that are similar to another properties photos. An ‘im‑
age2vec’ vector space was built from images and the service navigated that space
• Created Machine Learning pipeline in collaboration with infrastructure team within HomeAway to aid model production and deployment
• Created Data Science Internship Program for HomeAway University
• Using single compute nodes and later Spark, sole contributor to models that compared geographic representation of HomeAway properties
with the representation from a competitor. Initially, Natural Language Processing techniques such as TF‑IDF vectors were built from property
descriptions and distances measured between those vectors determined duplicate or unique properties, but LDA distributions were leveraged
as the project matured. A similar transition from single compute resources to Spark occurred as the need for greater parallelization became
apparent
• Bayesian and frequentist multi‑variate test analysis in R
• Introduced natural language processing, machine learning, and defensible statistical inference
• Multiple team and individual presentations on ML efforts and affects to C‑level audiences
• Created and led 100+ volunteer “science club”, all trying to swipe right for science
• Acted as an external ambassador through public speaking at various events. Built internal credibility around a scientific method to AB Testing
and algorithmic approached to business problems
• Collaborated with Data and Software Engineering organizations to prototype and select big‑data solutions based on Hadoop (HDFS, Hive) and
Spark
• Leadership role in Introduction to Data Science courses, Mentorship Program, HADES
• Reported to CIO
HomeAway, an Expedia Company
DiRECTOR, SOFTWARE ENGiNEERiNG
Jun 2010 ‑ Jul 2014
• Directed 25‑person team responsible for the Orders and Payments Platform services. The team consisted of Engineers and Architects of all
levels and areas in “the stack” (UX/UI, web‑tier, service layer, ...)
• Worked closely with CFO, CTO, and CPO as the services handled much of HomeAway’s revenue
• Led development and enhancement of PCI Compliant “payment island”. Architected and implemented payment gateway router which sends
transactions from clients to any one of several payment gateways (e.g. Cybersource, Payflow Pro, Yapstone, and Trident)
• Established HomeAway Inventions Program, which is responsible for identifying, cataloging and protecting intellectual property. Sole‑ or Co‑
Inventor on several pre‑patent inventions
• Technical member of global payment processor negotiation team, responsible for technical evaluation and diligence for HomeAway’s successful
selection of a global payment processor
• Team Lead delivering Unified Payments Services, PCI and SOX compliance, and migrating vertical payment flows to common service‑based API
• Two patents on data storage were filed and approved (see details in Patents section)
PayPal, Inc
PRiNCiPAL SOFTWARE ENGiNEER
Mar 2007 ‑ Jun 2010
• Implemented two‑factor authentication system (via hardware token) for PayPal and eBay
• Formed small team to introduce Agile Software Development methodologies to PayPal, received executive support and developed training
material
• (Certified) Scrum master for next‑gen (now current gen) view‑layer technology
PayPal, Inc
ENGiNEERiNG MANAGER, SOFTWARE DEVELOPMENT
Mar 2008 ‑ Jul 2009
• Led development of the content management system for PayPal’s marketing department
Visa, Inc
SOFTWARE ENGiNEER
Nov 2000 ‑ Mar 2007
• Managed development team delivering Corporate Card Expense Reporting service for large, multi‑national financial institutions
• Implemented central directory server for Verified by Visa
• Developed reference standard Directory Server for Verified by Visa(tm) implementation of the Three D Secure payment protocol
• Led testing and qualification efforts for Verified by Visa(tm) certifications
Education
JUNE 2023
BRENT SCHNEEMAN · CURRiCULUM ViTAE
3The Santa Fe Institute
COMPLEX SYSTEMS SUMMER SCHOOL
2015
University of Southern California
BS ‑ ELECTRiCAL ENGiNEERiNG
1990
Carroll College
BA ‑ MATHEMATiCS
1990
Volunteering
City of Austin Business & International Affairs Focus Group
INDUSTRY CONSULTANT
Jan 2018 ‑
• Focusing on Austin TX’s outreach to both business and international affairs
Skillpoint Alliance
CHAiRMAN OF THE BOARD, BOARD MEMBER
Feb 2012 ‑ Sep 2016
• Skillpoint Alliance builds partnerships between industry, education, and the community to provide a more qualified workforce for Central Texas
Certifications
Nov 2017 Neural Networks and Deep Learning (Python / Keras)Coursera
Sept 2014 R ProgrammingCoursera
Sept 2014 The Data Scientists ToolboxCoursera
June 2008 Certified ScrummasterRally, Inc.
Technical Expertise
Deep Learning
Engineering
Languages
Leadership and Strategy
Machine Learning
Supervised Learning
Unsupervised Learning
CNN, RNN, LSTM, Active Learning, Transfer Learning
REST, Object Oriented Design, Test Driven Development
Python, R, SQL, Java
Executive collaboration, Organization management, Culture change
Tensorflow, Keras, Pandas, Tidyverse
Regression, Random Forest, Gradient Boosted Trees
K‑means and hierarchical clustering, PCA, tSNE
Patents
USPTO
US9928498B2 SYSTEM, APPARATUS AND METHOD FOR SEGREGATiNG DATA iN TRANSACTiONS ViA DEDiCATED iNTERFACE
ELEMENTS FOR iSOLATED LOGiC AND REPOSiTORiES
2018‑03‑27
USPTO
US10339487B2 SYSTEMS AND METHODS TO RECONCiLE FREE‑TEXT WiTH STRUCTURED DATA
JUNE 2023
BRENT SCHNEEMAN · CURRiCULUM ViTAE
2019‑07‑02
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