- Leading a team of 10 data scientists applying ML and causal inference to reveal the drivers of customer growth, retention and lifetime value on Max.- Developed and deployed predictive metrics to quantify downstream impact on retention and LTV from new product features and marketing efforts.- Optimized churn deflection strategies via uplift modeling, identifying the best offer to present to each customer based on their recent engagement patterns and history with the service.

- Managed a team of 8 data scientists responsible for the analytical models & methods used to understand and optimize the customer experience at Nordstrom.- Developed Nordstrom’s Customer Attribute Library: the standard source of well-governed customer-level data for analytics, ML, audience targeting & personalization.- Built the analytical methods and predictive models to assess long-term customer value at Nordstrom, including causal modeling of incremental value from engagement milestones.- Owned predictive customer models end-to-end, from requirements gathering to deployment in production with automated monitoring and formalized incident response.Specific tech used: xgboost, causalml, prophet, teradata, presto, spark, jupyterhub, airflow, aws

- Defined and drove adoption of best practices in experimentation across the customer lifecycle at Hulu, from subscriber acquisition to engagement optimization.- Collaborated across business functions to define roadmaps for both data science and engineering as we built Hulu’s experimentation program from the ground up.- Led a team of data scientists, analysts and engineers in the development of tools and pipelines to standardize and automate every aspect of experiment design and analysis.

- Led data science initiatives across the company, balancing short-term client commitments and sales support against research to inform long-term product direction.- Scaled the data science function at EnergySavvy through hiring and coaching; drove awareness, adoption and expertise in scientific best practices across technical and nontechnical roles.- Developed an automated causal inference engine for utility customer data, providing timely, granular insight into the impact of customer programs.

- Developed and maintained cloud-based algorithms for home energy management, with robust performance monitoring via modeled energy savings.- Collaborated with UX to design the presentation of data-driven insights to consumers via mobile app and web portal, given statistical error and methodological caveats.

Site of the experiment on which I worked as a research associate with SLAC.

Conducted fundamental physics research at CERN as a member of the ATLAS collaboration,analyzing high-energy particle collisions in the Large Hadron Collider- Coordinated an international team of ten graduate students and postdocs.- Implemented robust background subtraction against time-dependent, correlated noiseto optimally identify and characterize scientifically relevant features in the data.- Communicated analysis methods and results to the larger scientific community, through detailed public reports and presentations at international conferences.

Site of the experiment on which I worked as a graduate student with the University of Washington.

Pursued doctoral research in particle physics at Fermilab as a member of the DZerocollaboration, analyzing high-energy particle collisions in the Tevatron- Trained random-forest classifiers to identify the collisions most likely to contain Higgsbosons, based on a large number of physical and topological features in the data.- Presented analysis methods and results at several international conferences.- Published final results in a leading peer-reviewed journal.