Machine Learning Engineer2019 - 2020Ibotta, Inc.
Technologies: SQL, Spark, Python
- Built an end-to-end framework for continuous validation of online recommender systems using Python, Spark, and AirFlow. Utilized best engineering practices leading to an extensible, highly-interpretable program.
- Contributed utility functions for common ETL tasks relating to online metric tracking via Python and DataDog.
- Analyzed recommender systems to understand the impacts of modeling decisions on downstream business metrics such as retention, re-orienting the team’s process, and improving future strategy.
Senior Growth Scientist2017 - 2019Eaze, Inc.
Technologies: SQL, R
- Owned delivery algorithms and invented key algorithm concepts, which improved ETA accuracy by 50%. Led relationships with product and engineering teams to plan and implement algorithm changes.
- Built a self-service experiment analysis and visualization tool empowering the marketing team to run email experiments in seconds rather than days.
- Automated driver shift planning models to reduce driver costs while maintaining delivery times.
- Advised and guided other data scientists on mathematical models and relationship building with other departments, leading to improvements in team output and cohesion.
Associate Director, Data Science2013 - 2017HomeAdvisor, Inc.
Technologies: Microsoft Excel, SQL, R
- Earned multiple promotions from data analyst to technical product manager and finally associate director in December, 2016.
- Founded, recruited, led, and managed a six-person data science team which made key optimizations to the matching engine and increased the ROI of marketing spend using regression-based analysis.
- Owned matching algorithms and invented new ones utilizing machine learning (regression and tree- based methods) that improved net revenue by 5% and customer contact rate by at least 25%.
- Developed an in-house test design and analysis platform on par with enterprise software that typically costs hundreds of thousands of dollars per year.
- Managed cross-functional projects with teams from technology, marketing, product, and finance to ensure predictive algorithms met the needs of all stakeholders.