David Mcgarry Email & Phone Number

Louisville, CO, US

Satellite Beach, Florida, US

• Homebound | Machine Learning Engineer (July 2021 - Present)
• Leading Homebound's machine learning and pricing applications teams.Gitcoin | Data Science Technical Assessment (July 2023 - September 2023)
• Conducted technical interviews and made hiring recommendations for Gitcoin's first two data scientist hires.Justice Reskill | Chief Technology Officer (February 2021 - June 2021)
• Acted as the Interim CTO for Justice Reskill, an early stage non-profit startup that seeks to advocate for, support, and train justice involved individuals with a focus on landing them jobs in tech.
• Played an integral role in forming the organization’s culture, engineering processes, and strategic technical plan.
• Developed the engineering department’s career pathing, hiring strategy, and interview playbooks.

Boston, Massachusetts, US

• Researched, tested and implemented optimizations to the default settings of DataRobot’s AutoML algorithm, resulting in a ~15% reduction to cloud costs and wall-time at the cost of only a ~0.25% decrease in accuracy.
• Led the project and acted as the lead engineer for development of DataRobot’s unsupervised clustering product suite.
• Developed a highly efficient AutoML mode that trained models at half the cost and time with only a ~3% impact to accuracy.
• Built an AutoML mode that maximized accuracy for users who valued model accuracy over training time and cost.
• Managed the hiring funnel for MLEs, including: screening resumes, reviewing technical tests, and conducting interviews.

Louisville, CO, US

• Architected and led the technical migration from an unstable and cost-prohibitive data warehouse to a horizontally scalable Data Lake.
• Trained and implemented a recommendation system that resulted in a 90% lift in minutes of content watched over the existing collaborative filtering approach.
• Hired, managed, and built the data science and data engineering teams.
• Designed the multivariate testing and experimentation process that is used to evaluate the impact of all new product features.
• Built automated CI/CD processes that enabled data engineering and data science to seamlessly release python packages and data products into production.

Denver, Colorado, US

• Built, hired, mentored, and led a 23 person department of machine learning, data engineering, and data science teams.
• Defined the strategic vision for Ibotta’s Data, including: data infrastructure, internal data products, and data monetization opportunities.
• Oversaw the creation and production deployment of multiple business impacting ML products, including Ibotta's: personalized recommendation engine, audience targeting product suite, and internal A/B testing platform.
• Pioneered the migration from a data warehouse to a S3 data lake that leverages distributed big data tools like Spark, Hive, and Presto.
• Designed and built a recommender system that resulted in an incremental ~4M in annual revenue over the existing collaborative filtering based algorithm.

Austin, Texas, US

• Launched, hired, and managed two R&D data science teams, one full-stack team that built SaaS and DaaS products, and another that developed internal machine learning data products.
• Spearheaded the career development and mentorship initiative within the Data Science department.

Austin, Texas, US

• Redesigned the methodology and predictive data features for the ODC’s high revenue product, resulting in a 100% lift in accuracy and 300% improvement to computation costs.
• Developed machine learning models that determine a household's propensity to purchase close to 200 different categorizations of CPG products.
• Built machine learning models that determine a household's likelihood to experience 9 different life events in the near future.
• Built an efficient machine learning system using Spark and Python that improved the daily assignment of demographics for 1.5 billion cookies by 200-500%.

Boston, Massachusetts, US

• Defined the strategic vision for the four teams within Return Path’s Data Science and Analytics department.
• Drove the strategy for Return Path’s Data Organization by working alongside senior leaders in engineering, product management and marketing as a core member of the Data Senior Leadership Team.
• Oversaw, managed and mentored 15 data scientists, statisticians and data analysts spread across the four teams in the department.
• Provided the context and framework for the teams within the Data Science and Analytics department to deliver on business-outcome oriented goals.

Boston, Massachusetts, US

• Defined and launched Return Path’s Feature Engineering team, which became a role model in the organization within its first quarter.
• Managed, mentored and hired a team of data scientists that focused on the efficient development of scalable machine learning models.
• Leveraged data from trillions of emails to build machine learning driven business solutions as Return Path’s lead data scientist.
• Collaborated with Data Engineering to develop a process using Kafka, Storm and Thrift to deploy machine learning models that consume hundreds of millions of raw emails a day in real-time.
• Routinely gathered, interpreted and scoped stakeholder requirements, developed the department’s first year-long rolling roadmap and participated in the Data Team’s quarterly agile planning as the acting product manager.
• Led the department initiative to define measurable team-based KPIs and surface them to senior leaders in the company via dashboards.

Boston, Massachusetts, US

• Led the technical components of a R&D project by working closely with clients and Executive team members to define and develop the Return Path’s first data-as-a-service product and third line of business, Consumer.
• Built a machine learning classification model with Python that efficiently categorizes hundreds of millions of purchased products into 50+ categories.
• Created a PII redaction and re-identification risk process for a monthly data feed of tens of millions of rows.
• Devolved prototypes of consumer loyalty metrics using unsupervised learning methods as part of a R&D project.
• Designed and implemented an improved new hire on-boarding system and department wide career development program.
• Mentored and trained a team of aspiring data scientists and statisticians.

Boston, Massachusetts, US

• Developed a collection of predictive models in Python that efficiently classify the content of millions of email messages every day.
• Created and automated the daily assignment of mailbox type categorizations to millions of email addresses using unsupervised clustering algorithms in R.
• Built a predictive model and accompanying report that identifies our clients’ churn risk.
• Designed and built multiple interactive data analysis web applications that have enabled internal staff to quickly and easily make better informed decisions.
• Founded a predictive modeling challenge that teaches practical data science to staff across the company and provides crowdsourced solutions to valuable business problems.
• Developed and taught weekly instructional sessions for colleagues on topics including: efficient programming in R and Python, machine learning, statistical analysis and project management.

New York, NY, US

• Managed all quantitative research services at NYU’s Data Services, including a team of 2.5 full-time staff members and 7 graduate student consultants.
• Provided statistical computing assistance to faculty, staff and student researchers as the Data Service’s lead quantitative programming consultant.
• Designed lessons, wrote training documentation and taught classes for statistical computing tools, including: R, High Performance Computing, SQL, SAS and SPSS.
• Conceptualized, recommended and implemented integral revisions to the Data Service’s service model, including the support of High Performance Computing, SQL and Python.
• Designed methods for the collection, analysis and reporting of internal usage metrics for the Data Services and High Performance Computing Clusters.

New York, NY, US

Ms, Information Systems

Ba, Statistics