Data Scientist and Machine Learning Developer in Oakland, CA, United States

Acted as a lead data scientist for QuickBooks Online's self-help recommendation app, which required a multi-team collaboration. The goal was to surface the most relevant help articles to customers and enable them to resolve their problems from within the product. My role was to build and integrate the ML engine.

For data exploration, extraction, and feature engineering, I liaised with data science and data engineering teams to understand the multiple sources of relevant data. I wrote efficient PySpark code to ingest and transform high volumes of clickstream (billions of rows), customer profile data, and help article databases.

For model training, I employed a novel deep learning approach consisting of shared layers, LSTMs, and merging temporal sequences with static features.

To productionize the model, I led a team consisting of other DS contributors as well as front-end and back-end developers, and members of performance testing and A/B testing teams. Together we integrated the model with the existing click data streams, built I/O specs, ensured adequate stability and response latency, and measured significant improvements in customer engagement (55% higher clickthrough on articles) and support metrics (10% lower call rates).

Served as a lead data scientist and technical lead on a Scrum team analyzing videos of high-resolution neural tissue microscopy data, with members from biology, robotics, software, and data science. The goal was to build, validate, and enable CI/CD for the pipeline, and use it to measure the effects of pharmacological perturbations, lab protocols, and genetic modifications on the activity of networks of artificially grown neurons.

The key challenge was representing extremely high dimensional data (high spatiotemporal resolution) via low-D, biologically interpretable metrics. We built a 12-module semi-automated data processing graph (DAG), including supervised and unsupervised CV methods constrained by biological priors, to clean and standardize the data and intermediary QC steps that were automatically triggered and triggered the final stages.

Deployed as a streaming app in AWS on over 10TB of data, it reduced QC-ed videos' failure rate by 75% and enabled us to increase the temporal resolution 10x.

For analytics, I designed two novel ML-based methods to deconfound experimental variables and produced several critical endpoints, including distinct effects of three different drugs and significant accuracy in predicting disease.