Experience

EarnBetter

• United States
• Technology, Information and Internet
• 1 - 100 Employee

• Head of Artificial Intelligence

  • Aug 2023 - Dec 2023

  I led the AI side of things at Earnbetter, which included building, testing, and deploying new recommendation algorithms using LLMs, embeddings, and fine-tuned open source models.



Indeed.com

• United States
• 1 - 100 Employee

• Director of Machine Learning and Data Science

  • Mar 2021 - May 2023

  Seattle, Washington, United States I led a team of 50+ Scientists within the Job Seeker group at Indeed, including analysts, machine learning engineers, deep learning, statistical, and optimization experts. I helped grow the team, hiring leaders in deep learning, senior managers, senior ICs, and created the Science interview process at Indeed. The team spanned across Japan, India, Singapore, Seattle, San Francisco, Austin, and had a large remote group. The team, at one point included 10 managers. I led Science processes… Show more I led a team of 50+ Scientists within the Job Seeker group at Indeed, including analysts, machine learning engineers, deep learning, statistical, and optimization experts. I helped grow the team, hiring leaders in deep learning, senior managers, senior ICs, and created the Science interview process at Indeed. The team spanned across Japan, India, Singapore, Seattle, San Francisco, Austin, and had a large remote group. The team, at one point included 10 managers. I led Science processes, including promotion, role transition, interviewing, and leveling and promotion rubrics in addition to providing technical expertise on machine learning. I led a company wide ML Platform group, including Scientists from across the company and leaders in Engineering roles. We set a 2 year development plan on how to build a more robust ecosystem of ML Platforms across the company that could meet latency, flexibility, and ml ops SLOs.

• Senior Manager of Data Science

  • Jan 2018 - Mar 2021

  San Francisco, CA, Mountain View, CA I led Science roles across multiple product teams at Indeed, eventually transitioning into a role that had the scope of the Job Seeker group (a 200+ person engineering group with 35+ Scientists). I led Scientists across multiple offices across the globe, visiting Tokyo and Singapore to onboard new Scientists and leaders. I provided technical guidance, and leadership to the team and with Product and Engineering leaders across the company. Two major teams grew substantially in this time period… Show more I led Science roles across multiple product teams at Indeed, eventually transitioning into a role that had the scope of the Job Seeker group (a 200+ person engineering group with 35+ Scientists). I led Scientists across multiple offices across the globe, visiting Tokyo and Singapore to onboard new Scientists and leaders. I provided technical guidance, and leadership to the team and with Product and Engineering leaders across the company. Two major teams grew substantially in this time period within my scope, including the DSP (Data science platform) team, which supports ~40% of all deployed machine learning solutions at Indeed, and the JEM (job seeker employer match) team which built applicant quality models (predict the fit between a job and a job seeker). We worked on models ranging from decision tree ensembles (xgboost, lightgbm) to more sophisticated deep learning models (including twin tower solutions and solutions built on top of LLMs (BERT)) I gave external talks at ICLR and O'Reilly Software Architecture covering ML Platform related topics and presenting on a panel of novel machine learning solutions related to job seeker <> employer matching models.

• Data Science Manager

  • Jan 2016 - Dec 2017

  San Francisco Bay Area I began managing after working as a staff level Data Scientist and took on a reporting of both Software Engineers and Data Scientists on my team. I helped multiple teammates grow their careers through promotions and technical leadership opportunities. My team size was typically ~ 4-6 during this time, eventually including managing managers. I also worked as the technical lead for multiple teams during this time, creating, staffing, hiring, and writing OKRs, JIRA process, and agile kanban… Show more I began managing after working as a staff level Data Scientist and took on a reporting of both Software Engineers and Data Scientists on my team. I helped multiple teammates grow their careers through promotions and technical leadership opportunities. My team size was typically ~ 4-6 during this time, eventually including managing managers. I also worked as the technical lead for multiple teams during this time, creating, staffing, hiring, and writing OKRs, JIRA process, and agile kanban based approaches. I led planning, OKR setting, and presented progress to stakeholders. My team delivered incredible impact to job seekers by reducing friction during the apply process.

• Senior Data Scientist

  • Sep 2013 - Jan 2016

  Austin, Texas Area I train, test, design, and implement machine learning models to analyze patterns and predict trends to help job seekers find careers. My work is primarily done in Java and Python.



USAA

• Financial Services
• 1 - 100 Employee

• Data Scientist

  • Jul 2012 - Sep 2013

  San Antonio, Texas Area I work on developing and providing business insights using large amounts of data and machine learning techniques.

• Software Developer

  • Aug 2011 - Jul 2012

  San Antonio, TX I develop and support Wicket Java applications for Investment products at USAA.



Laboratory for Atmospheric and Space Physics

• United States
• Research Services
• 300 - 400 Employee

• Software Engineer

  • May 2007 - May 2011

  I developed software on the Planning and Scheduling team. An important part of mission operations is ensuring that the instruments take the right scientific data at the right time. The Planning & Scheduling team functions as each instrument’s time-management assistant, determining where the instrument should point, when it should take data, and what types of data it should take. To keep track of each spacecraft’s schedule, the team uses specialized planning and scheduling… Show more I developed software on the Planning and Scheduling team. An important part of mission operations is ensuring that the instruments take the right scientific data at the right time. The Planning & Scheduling team functions as each instrument’s time-management assistant, determining where the instrument should point, when it should take data, and what types of data it should take. To keep track of each spacecraft’s schedule, the team uses specialized planning and scheduling software. The software we use the most, which we developed here at LASP, is called the Operations and Science Instrument Support Planning and Scheduling system (OASIS-PS). OASIS-PS is capable of automatically generating operations plans based on rules and constraints programmed into the software or stored in the supporting database. Operations activities can be displayed on a timeline and operators can add, modify, or delete activities from the timeline. I developed software using Java 6, Python, SQL, for Unix, and Mac OS's. Show less