Led the NLP Engine team that allows Alexa to understand the user's intention.- Worked on every aspect of machine learning: from feature engineering, data annotation, and model training, to runtime execution and operational management of hardware and services on a global scale- Managed 14 software engineers: (9 direct reports, 1 subordinate manager, and 4 indirect reports.) and ~3 interns per year- Consistently had the organization's highest engineer satisfaction ratings and lowest turnover rates- Developed a talented team that earned promotions for 4 sr. engineers, 5 mid-level engineers, and one manager- Hired talent at all levels from interns to sr. engineers- Managed underperformance, with only one process leading to termination- Worked with internal and external stakeholders (e.g. Disney, Microsoft, licensed software providers, etc.) to set, track, and accomplish product level goals

Worked with several different teams to design and implement substantial portions of the NLP engine.- Automated the machine learning process including: data preparation, training, evaluation, deployment to production systems, automated A/B experimentation against current models with automated promotion to production- Coordinated multi-team projects through scope management, risk estimation, outcome estimation, and reporting- Implemented real-time metrics instrumentation and automated emergency mitigation strategies, provided on-call operational support for Alexa services- Maintained a high quality codebase through extensive design and code review

Worked on a small, multi-discipline team of engineers, scientists, and linguists: created Alexa's first system capable of understanding conversational context.- Designed and implemented the procedural rules engine that backs Alexa's context systems- Created the team's framework for automated functional and integration testing- Built annotation tooling to generate labeled datasets for statistical model training

Worked as a software engineer to develop a system for a voice activated television remote control.- Built on both the device hardware (C++) and in the cloud (Scala) linked via a GRPC framework (an early version of Google's Protocol Buffers)- Utilized topic / intent classification, named entity recognition, word-sense disambiguation, and dependency parsing to extract desired action and specific request details from voice input.- Implemented algorithms as Scala APIs for consumption by the voice service.

Dramatically increased the efficiency of human annotators by creating a system to identify patients diagnosed with Stage IV lung cancer, using the free text of clinical notes.Presented at the American Medical Informatics Association 2015 Joint Summits on Translational Science.

Performed quantitative and qualitative data analysis to assess the impact and outcomes of Treehouse programs.Interpreted results and created reports on a monthly, quarterly, annual, and ad-hoc basis.Managed data life-cycle, including collection, coding, entry, validation, cleaning, analysis,and reporting.Developed, deployed, and maintained a relational database to contain Program Evaluation departmental data.Entered, verified, and organized large data sets; performed data transfers to and from other agency data tools.Supervised department interns and volunteers.Analyzed, and reported on data under the direction of the Evaluation Manager.