I'm a senior software engineer at Netflix, where I lead the firm's Global Real-time Guardrails initiative and serve as interim lead engineer for the Data Foundations Squad within the Experimentation and Causal Inference Platform. Guardrails runs automated canary analysis during Big Bet live events and continuous deployments, and Data Foundations owns the pipelines underneath the platform. In both roles my job is the correctness and reliability of the data and tooling Netflix data scientists depend on. I'm also a co-Informed Captain for the platform's generative AI strategy, and I've designed agent-based systems now running in production.
Before Netflix I spent more than a decade in finance building electronic trading systems, at both Citadel (the hedge fund) and Citadel Securities (the market maker). At Citadel I built charting libraries and real-time dashboards for the Global Commodities and Global Equities desks, the order entry application the whole firm uses to execute live market orders, and a Python library that puts an interactive chart on screen in three lines of code.
At Citadel Securities I built the desktop trading applications for the ETF Market Making desk, where every manual equity and commodities trade ran on software I wrote, and the RFQ system for the Options, OTC, and ETF desks, which held up through the COVID crash of 2020. A market anomaly detection system I built there has contributed several million dollars in incremental PnL. Both firms ran KDB+ and q in production, so much of my work was streaming real-time and historical time-series data into trading applications and writing Python tooling that let quantitative researchers query KDB+ from Jupyter. Earlier in my career I held engineering roles at Goldman Sachs and Morgan Stanley, quantitative analyst roles at BlackRock and J.P. Morgan working on structured finance and exotic fixed-income derivatives (swaps, swaptions, synthetic CDOs), and an institutional technical sales role at S&P Capital IQ.
I've spoken at Node Congress, JSNation US, JSConf India, and DevOps.js, and given invited talks at the White House, the United Nations, Harvard Business School, Columbia, NYU, and the Japan Information Technology Services Industry Association. I wrote Learn Algorithmic Trading with Python (Apress/Springer) and the open-sourced TensorFlow.js Quickstart Guide. My papers cover portfolio optimization, reinforcement learning for trading, and how large language models handle programming tasks.
My wife Felicia and I started Code Crew in 2013. It began as a weekly study group in New York City coffee shops and now runs to more than ten thousand members across two meetup groups, and we've taught thousands of people to code through it. That work has been covered in TechCrunch, Fortune, Forbes, CNN/Money, and Black Enterprise. I've also taught web development at Columbia, General Assembly, and Startup Institute, and at the New Jersey Institute of Technology, where I worked with the mayor's office, the governor's office, and Code for America to develop and open-source the curriculum.
I hold a B.S. in Economics with a minor in Business from Penn State, where I was a Schreyer Honors Scholar and a Ronald E. McNair Scholar, and a Graduate Certificate in Business Analytics from Harvard Business School's Business Analytics Program (graduated with Distinction). I'm currently finishing a Master's in Data Science and Artificial Intelligence.
Outside of work, algorithmic trading strategy development is a long-running hobby, mostly in Python and Rust these days. I shoot photography on FujiFilm (after many years on Canon) and I follow film closely enough to make it to the Tribeca Film Festival and the Toronto International Film Festival every year. I once pitched a television series co-produced by Michael K. Williams, worked on independent films with Issa Rae, and toured for a year with a Grammy-nominated Universal Music Group recording artist. I live in Westchester County with my family and was born and raised in New York City.