Student of many codes, master of one - pure Python full stack developer
Building ETL pipelines for big data analytics
Creating intelligent applications and real-world, interactive AI automation
Building recommender systems and pricing analysis tools, models that drive business
Passionate polyglot programmer and technical product manager.
Serving cross-functional roles in data science, I lead R&D for a team of machine learning engineers building cognitive, smart automated solutions and platforms bringing data science products to market.
Product Manager, Interactive Artificial Intelligence Team
NAST Data Science & Analytics, C.H. Robinson Worldwide
Collaborate with executive leadership to measure opportunities and KPIs, determine RICE score models for prioritization, architect application prototypes and data ETLs, provide automation guidance and expertise on behalf of data sciene to teams across North America, among numerous other concurrent responsibilities serving as product manager for the NAST Data Science Interactive AI team of machine learning engineers.
Software Engineer (Full Stack), Data Science & Analytics
Build MVP & prototype solutions in rapid iteration, determine initial software and database architecture, scalability planning, microservice prototyping, data ETL, and application deployment patterns. I design, code, test, and maintain an extensive stable of automated applications, both at the platform level and custom AI solutions built to team spec for highly technical, specific application.
I work at the intersection of IT, data science, and business strategy to deliver bleeding-edge machine learning and intelligent automation and applications to North American Surface Transit (NAST) business groups. Following agile methodologies, I prioritize work for the team's machine learning engineers, coordinate with integration managers and data scientists, and lead the overall strategy to achieve quarterly KPIs. Our highly-visible group is responsible for building the future of Robinson, the world's largest logistics provider.
I'm actively working, building and managing concurrent projects in the following pillars.
Architecting integrated NoSQL and relational databases for streaming and archival data
Growing machine learning and technical teams in the product and capability space
Building scalable pipelines specializing in data extraction and transformation using streaming and static assets
Generating insight from data and analytics while delivering real-time visibility to executive teams
Supervised applications that enhance mechanical human processes to profitable outcomes
Building streaming and static data pipelines in a big data environment
How many years of experience do you currently have as a software engineer?
I have been a professional Python software engineer for over 10 years. Prior to that I worked for six years as a PHP developer and SQL database engineer.
Why be an engineer in data science?
Data science is incredibly important and increasingly essential for both small and large companies now more than ever. But all that knowledge and insight is useless unless it can be curated, refined, and delivered to users in automated ways. As an engineer, I get to see the 'AHA!' moments when users interact with our applications and that's what drives me to build impressive technologies.
What is one of the most impressive products you've built?
Designing and programming an intelligent, automated solution for large scale e-commerce catalog pricing management. This machine learning application captured and monitored pricing by SKU from competitors' websites, pulling in custom pricing dials managed through an executive dashboard, and re-priced entire product catalogs in Magento 2, Oracle NetSuite, and also updated pricing across Google Shopping. This all-in-one application allowed management to use machine learning to analyze competition pricing, showing seasonal and other trends, while automatically re-pricing sometimes tens of thousands of products by pennies to beat the competition and remain number one on Google Shopping near real-time.