Tech lead for AI Analyst Team. AI-driven portfolio management for top VC/PE firms like Bessemer, General Catalyst, and Accel, along with 10,000+ portfolio companies.

• Owner for core financial metrics/KPI engine, powering VCs to track portfolio performance. Improved validation with database triggers, developed interfaces for writing/retrieving metrics across the app, and architected data pipelines for sharing and syncing millions of metrics

• Owned internal infrastructure and tooling, building multiple data importers that our services team relied on for customer implementations, libraries for Postgres data anonymization and S3 file exports, and more

• Worked on refactor of our cap table management product, boosting average P50 and P99 endpoint performance by over 60% while simultaneously implementing a library for safely double writing across multiple DRF API endpoints

• Rolled out static type-checking across to over 20% of our Python codebase with Pyright, reducing typing-related errors by over 40%, while automating the process of annotating 100% of our legacy code by generating types from our test suite

• Member of the Venture team

• Utilized Python/Django with OpenPyXL to add full currency conversion support in our platform’s Excel exports for customer’s financial metrics and investment details along with adding settings to configure this using React

• Implemented granular edit and view permissions system across all of our application’s pages and APIs utilizing Python/Django, React, and TypeScript along with adding comprehensive frontend and backend unit testing coverage

• Built out new zip export system that enables downloading files from S3 while allowing for arbitrarily large exports and avoiding persisted file storage utilizing Python, TypeScript, Boto3, and Celery queues

• Member of the Logs Analysis and Logs Efficiency teams

• Build out support in the Logs testing framework to support multiple CPU architectures

• Increased C++ Logs Analysis API test coverage (unit and integration tests) for rollout of new features

• Designed dashboards with Google Data Studio using SQL to visualize and track metrics that show the growth of new feature usage and Logs efficiency gains

• Utilized frameworks and libraries such as gRPC, Protocol Buffers, GoogleTest, and Abseil

• Member of the Geo Offers team which ingests deals from businesses and surfaces them on Google Search/Maps

• Worked on analytics and prediction models for the engagement of deals served to users

• Utilized Python with pandas, NumPy, SciPy, and Matplotlib to clean and analyze a dataset with 100k+ entries

• Designed, implemented, and trained a machine learning model using scikit-learn to predict whether the click through rate of a given Google Search/Maps deal will be high or low for the purpose of ranking

• Navigated the process of model research and feature engineering, starting with a machine learning model with 1% accuracy and reaching a model with 94% accuracy and a 75% recall score