One-pager highlights of recent work experience
Developed the centralized reporting dashboard and infrastructure for C-Suite visibility into company-level metrics such as: Ease of Use, Pace of Interactions, Time to Value, Number of Interactions, Monthly Active Users, Daily Active Users, Monthly Active Brands, Jira Pulse SLAs, Data Center Availability, Service Level Objectives, Security Patching, and 20 other main metrics used for criticial success measurement across Product, Experience, and Engineering. Accomplished with Python, Prefect, and Tableau.
Leading critical operational metrics of 50+ different KPIs visible to Engineering leadership and C-Suite. 40% of all engineers in the company have interacted with Eng Analtyics data products and 35% of the entire company overall. For our most recent quarterly review, a majority of users were saved a great deal of time and had a great deal of decision making process influenced by our Tableau dashboards, Redshift tables, and Airflow DAGs.
Managed mission-critical operation of operational review metrics used in realtion to Jira data. Major investigations and realtime reporting on customer pulses, deep dives on product quality feedback, and analyses of backlog management across all Engineering teams.
Investigated OKR adoption practices and made a company-wide change for OKRs to be more tightly linked via manager-direct objective-result pair linking.
Identified 140 brands to be targeted by the Qualtrics Preview Program with a net revenue lift of $14M+.
Led an investigation into pre- and post-pandemic Engineering efficiency metrics and found that Qualtrics transitioned with no statistically significant impact on developer productivity. Led initiatives on data governance to establish the adoption of Alation and data documenation best practice updates.
Led an initiative to bring analytics team across the company together for informal tech talks.
Led a team of data scientists and data engineers to build a multi-touch attribution model in Google Bigquery and R. Owned and maintained data sources critical to the Marketing organization’s daily operation. My team’s projects had visibility to executives including user journey path analysis, realtime dashboarding for major product launches, and predictive customer lifetime value models. Handled data science interviewing, onboarding, and intern transitions to full time employment.
Worked across many teams including Visual Studio Team Services, Azure, Engineering Customer Interactions, Data & Decision Sciences. Developed data engineering pipelines in Kusto, Dax, and SSIS to provide high-visibility insights in PowerBI to stakeholders in numerous product organizations including Legal, Xbox, and Support.
Led trainings on data science and analytics best practices with R, SPSS, SAS, and various data visualization tools.
- MSc Astrophysics, University College London, 2012
- BS Physics, Western Washington University, 2010
Introductory text book on the basics of machine learning with the R language. Topic covered include regression, classification, neural networks, tree-based models, and deep dives into common machine learning packages that are used with R. Used in many univeristy courses on statistics.
Alation, Apache Airflow, Apache Superset, AWS Athena, AWS Aurora, AWS Redshift, Amplitude, Git, Google Analytics, Google BigQuery, Jira, LATEX, PowerBI, Python, R, Splunk, SQL, Tableau, Testrail