Kristen Keller

Leading two teams - Our AI Platform team, which builds AI/ML tooling and infrastructure that accelerates 15+ other product and project teams. - A group of AI/ML engineers who work with different product teams across the company to tackle high leverage problems such as personalization and recommendations.

Redesigned ML model training and deployment templates amid a multi-year infra migration. Worked on Zapier's first major AI-powered product that enabled users to configure workflow automations using natural language. Tools: Python, Django, LLMs, NLP, Typescript, SQL, Airflow

Worked with marketing, product, UX, and engineering leadership to create and execute on a roadmap to increase the number of merchants who are using Shopify Logistics for their logistics needs. As part of this effort, the team shipped both user-facing and stakeholder-facing products in areas such as marketing personalization, automatic detection of products that are ineligible for our services (using text and image data from product descriptions), and identification of high fit/high intent leads. Tools: Spark, Python, SQL, GCP, dbt, Oozie/Airflow, Docker, git, NLP

Worked on a backend service that powered the website experimentation platform to extend its capabilities beyond simple AB testing. This work enabled product managers to run multivariate experiments as well as experiments that used contextual information about the user to choose the best treatment using our platform. Tools: Python, Flask, AWS, multi-armed bandits, contextual bandits

Worked on AI & ML powered services that were integrated into our core product. This ranged from systems that parsed unstructured text to enable better filtering in our vehicle search, to personalized routing engines that nudged users toward pages where they were more likely to submit leads as they browsed. Advised 6+ analysts, full stack engineers, and data scientists working on machine learning projects such as churn modeling and extraction of text data from complex pdfs. Presented results at ~10 internal tech conferences, engineering brown bags, and weekly all company meetings. Placed first in one company-wide hackathon and second in another. Piloted tools that were rolled out across the org such as MLflow and helped other teams adopt them. Tools: Python, Spark, SQL, NLP, Machine Learning, Flask, AWS

Owned the onsite ad forecast that our sales reps used to determine how many ad impressions were available to be sold for the next year. Created a forecast that helped to guide the transition from impression based monetization to click and action based monetization. This allowed us to identify areas where the new click and action-optimized ad portfolio was likely to underperform and adjust our strategy before the full rollout. Tools: Spark, R, SQL, Databricks, time series forecasting, regression

[Epidemiology Department] Used statistical models to examine whether systemic inflammation during early childhood is associated with breast cancer risk factors that emerge later in adolescence. Understanding this link can help inform preventive health interventions, improve monitoring of at-risk individuals, and reduce long-term cancer risk. Wrote reusable functions to automate tasks such as running models on different subsets of the data, identifying anomalous observations, and producing figures from model output. [Health Policy & Management Department] Used operations research to understand how efficient different countries are at converting resources that are invested into their healthcare systems into positive healthcare outcomes. This information could be used to identify which countries are making efficient use of resources and understand how their practices could be applied to drive better healthcare outcomes in other countries. Amassed data from multiple surveys on country-level healthcare systems and used that data to quantify the resources being allocated towards healthcare for this research. Tools: R, ggplot, regression, nonparametric statistics, operations research

Analyzed gene expression datasets containing 20,000+ genes each with the goal of identifying key genes that regulate environmental stress responses in plants. Understanding which genes regulate stress responses (such as responses to heat stress or drought) furthers to our ability to engineer crops that are more resilient to extreme weather conditions. Used nonparametric statistics to identify genes that are differentially expressed under various stress conditions and unsupervised clustering to identify families of genes with similar expression patterns (that likely work together). Presented results at 6+ departmental events and external research conferences. Completed a 41 page undergraduate thesis. Tools: R, unsupervised learning, nonparametric statistics, clustering

Evaluated a range of materials to understand which would be best suited for growing stem cells in a 3D environment. When stem cells are cultured in 3D environments (as opposed on flat plastic plates), this gives us a better understanding of how the cells would behave within a living organism. This is crucial for furthering our understanding of how to grow functional tissue grafts in a lab. Presented a poster on this work to a mixed audience with varying levels of technical knowledge at an internal research symposium.

Used image processing techniques to create 3D models of the HIV-1 virus based on 2D images. These 3D models could be used to identify potential drug targets and help researchers understand how new anti-virals might interact with the virus. Presented a poster on this work to a mixed audience with varying levels of technical knowledge at an internal research symposium.