Spearheaded performance initiative, creating and coordinating 50+ tickets across 9 cross-functional teams, achieving measured production improvements: UI Startup p75 -24%(-1.2s), p95 -40% (-6.5s).

Led React Compiler codebase-wide adoption end-to-end, delivering p95 UI Startup (-6.1s) and p95 Home Page -29% (-1.4s) — single largest improvement in performance initiative.

Identified broken selector memoization as pervasive failure mode, targeted selectors at root of dependency tree, and fixed getInternalAccounts triggering re-renders across 35 downstream selectors and 50+ components; guided Confirmations team's transaction selector implementation achieving cache hit rate 0.9% → 98.2% and 98% computation reduction.

Identified global cascading re-renders triggered by broken selector memoization in top-level components as causing 125-200 unnecessary re-renders per user action; reduced optimization scope to 5 high-leverage intervention points.

Expanded performance metrics collection to include user-perceived responsiveness metrics and analytical data for performance improvements including INP, TBT, React render time, and cache hit rate, response time, for network requests. Also suggested and implemented statistical validation for data points including z-score hypothesis testing, ANOVA corrections for cross-machine test results, coefficient-of-variance thresholds.

Authored org-wide Frontend Performance Guidelines establishing selector memoization patterns; proposed custom equality functions adopted into Principal Engineer's controller selector ADR.

Acted as security liaison for the team, directly applying security fixes, interfacing with the security team and feature teams to triage and resolve security alerts and vulnerability reports, and reviewing dependency capabilities allowlist policy changes to prevent supply-chain attacks.

Identified and optimized costly operations in mobile clients resulting in 76% improvement in UI startup time.

Defined Sentry custom spans providing granular insight into performance bottlenecks and focusing optimization efforts.

Extended internal tooling and wrapper functions for custom instrumentation around the Sentry SDK.

Authored the organization-wide TypeScript style guide, with a focus on advocating best practices that promote type safety and maintainability, and fostering a shared understanding of complex underlying concepts rather than imposing a rigid ruleset.

Decimated "any" usage count in the core monorepo from 806 to 72 by advocating for a concerted effort, achieving buy-in with the team, and leading progress on an OKR targeting "any" removal.

Extended the wallet framework that powers MetaMask's frontend clients by resolving complex and long-standing typing issues, contributing features and bug fixes, and consolidating conflicting module architectures.

Created a process guide with comprehensive step-by-step instructions on migrating libraries into the core monorepo, speeding up the process and reducing the complexity of subsequent migration efforts.

Streamlined developer experience with improvements in release management, yarn monorepo management, and build tooling.

Boosted conversion funnel metrics by 40% on user onboarding content by coordinating with product and design on a data-driven strategy for reorganizing content flow, based on Google Analytics data from phased rollouts and A/B testing.

Wrote custom React hooks to consolidate backend API fetch logic, and provide simplified interfaces for ensuring that complex state updates involving multiple Zustand stores do not trigger unnecessary re-renders in subscribed components.

Created a seamless cross-platform UX consistent with Figma mocks by extending a custom Styled Components library, and adapting components and UI flow across Electron desktop app, browser web-app, React Native, kiosk client environments.

Utilized React Portal, forwardRef, and Popper to implement responsive, adaptive positioning of floating elements.

Used Python NumPy, Pandas, Statsmodels, SciPy to sanitize, aggregate, transform large volumes of asset price time series data, check for normality, heteroskedasticity, and autocorrelation, and fit a ARIMA-GARCH(1,1) model for Value-at-Risk forecasting.

Utilized Seaborn, Matplotlibs for data visualization of time series analysis, diagnostics, forecasts across multiple timeframes.

Used Statsmodels methods to compare bidirectional Granger Causality of VaRs and test for risk spillover between US-Chinese markets.