# Barnett Yang > Quant Dev @ Cubist | Berkeley Honors CS/Math ’24 Location: New York City Metropolitan Area, United States Profile: https://flows.cv/barnett I am a quantitative software developer at Cubist Systematic Strategies working on trading infrastructure for the Cubist Crypto Asset Group. I originally joined Cubist as a member of the Quantitative Developer Academy. I received a double major in Computer Science and Mathematics with Highest Distinction in General Scholarship (summa cum laude) at the University of California, Berkeley in 2024 as an EECS Honors Student. In the past, I have interned at Citadel Securities, Amazon, and Sandia National Laboratories. ## Work Experience ### Quantitative Software Developer @ Cubist Systematic Strategies Jan 2024 – Present | New York, NY - Constructed Python, C++, and Go strategy containers and trading bots supporting crypto, futures, equities, and options trading; implemented pre-trade risk controls, handling of multiple symbologies, fee and financing attribution, risk/PnL/position React dashboards backed by PostgreSQL, and hourly fill/position reconciliation against Point72 back-office systems. - Developed stream-based DMA and order netting algorithms that minimized trading costs for futures and options carry strategies and allowed signals with disparate holding periods and aggressiveness to execute within the same trading area. - Engineered C++ L2 live and historical data pipelines, providing liquidity insights that accelerated creation of 5+ new alphas and powered smart order routers that increased trading returns by 5%. - Replaced unstable AWS MWAA with self-hosted Apache Airflow using deferrable operators to reduce worker demand; halved infrastructure costs and improved scheduler and DAG processing latency, webserver responsiveness, and system availability. - Created shared runtime environments distributed via NFS/AWS EFS and formalized after-hours application rollback procedures to maintain 24/5 system uptime. Environments were created via Github Actions and Jenkins SDLC pipelines. - Eliminated inconsistent research workflows by defining and implementing backtest methodologies that standardized individual researchers’ preferences; wrote unified React UIs for signal evaluation and portfolio manager weight allocation. - Served as primary liaison between CCAG and Point72 infrastructure/back-office; coordinated trading limits with Risk and Compliance and led crisis response during data, broker, and internal system and service outages. ### Software Engineering Intern @ Citadel Securities Jan 2023 – Jan 2023 | Chicago, IL - Pioneered a performant KDB in-memory HDB pre-caching API that accelerated slow on-disk retrievals 60x, enabling real-time integration of ad-hoc historical queries into trading, alerting, and metrics systems without cold reads; API was benchmarked against production datasets and demoed to the Head of Execution Services Technology. - Built KDB complex enrichment processors and C++ data feedhandlers that enhanced exchange/order visibility and migrated legacy C++ data transformers to a high-performance KDB architecture. ### Machine Learning R&D Intern - Math Analysis and Decision Science @ Sandia National Laboratories Jan 2021 – Jan 2023 | Albuquerque, NM - Composed a first-authored publication accepted for HICSS-56 proceedings discussing applications of Bayesian networks (BNs) in cybersecurity-attack detection, improving statistical robustness metrics by over 15% on imbalanced red-team cybersecurity datasets through methods in BN feature selection, random forest MDI discretization, and structural learning. - Designed a scikit-learn-compatible extension library that cut manual BN workflows by 90% and enabled BN integrations into existing ML pipelines, with adoption across multiple Sandia labs and projects. ### Project Manager - Atlassian @ PlexTech Jan 2021 – Jan 2022 | Berkeley, California, United States - Established project scopes with Atlassian product managers; set up developer sprints, onboarding projects, CI/CD pipelines, and code review workflows; and led implementation of a novel feedback-tracking application within Atlassian Compass. - Expanded the issue-tracking service to include an NLP-based text similarity microservice, hosted on AWS EC2 and RDS. ### Vice President of External Affairs @ PlexTech Jan 2022 – Jan 2022 | Berkeley, California, United States - Reformed semester recruitment processes of over 100 applicants via detailed recruitment timelines and member accountability measures, and decreased deliberation man-hours by over 50% through the creation of objective score-based assessment tools. - Organized UC Berkeley “Cal Intro to Tech,” a joint recruitment drive for nine student tech organizations with over 200 attendees. - Led planning of inter-organization networking and publicity events, including speaker events by LinkedIn managers. ### Software Development Engineer Intern @ Amazon Jan 2022 – Jan 2022 | San Francisco, CA - Prototyped and deployed services for repair operation (RO) transparency, helping return vendors verify RO proactively, reducing tech team workloads, improving operational efficiency, and establishing a proof of concept for a unified repair portal. - Architected RO services by evaluating tradeoffs between different AWS compute and storage options. Implemented deployments and integrations across AWS compute, authentication, CDN, React, and database services using AWS CDK. - Presented and demoed completed applications to directors of Amazon ReCommerce and Warehouse Deals organizations. ### Data Analyst @ DataGood @ Berkeley Jan 2021 – Jan 2021 | Berkeley, California, United States - Executed data analysis for the non-profit ViviendasLeon. Developed and maintained client relationships by translating business requirements to data science solutions. - Built machine learning classifiers and scoring algorithms to predict optimal crop recommendations, thereby informing rural Central American farming practices and volunteer data learnings through a Heroku API and Google Data Studio dashboards. ### Data Analyst @ UrsaTech Berkeley Jan 2020 – Jan 2021 | Berkeley, California, United States - Investigated the socioeconomic factors influencing student success and development under distance learning programs in the COVID era. - Work done in Python using data analysis libraries such as Pandas, Numpy, Matplotlib, Sklearn, Seaborn, Tensorflow, and Keras. - Cleaned, analyzed, and visualized datasets including the High School Longitudinal Study, the Household Pulse Survey, and the Current Population Survey. - Trained linear regression and neural network models to match the observed data. - Considered both the economic influences on academic performance and the economic impacts of the COVID-19 pandemic along racial lines. - Published findings on Towards Data Science with public code repository on Github. ## Education ### B.A. in Mathematics and Computer Science University of California, Berkeley ### King's College London ### High School Diploma South Pasadena Senior High School ## Contact & Social - LinkedIn: https://linkedin.com/in/barnettyang - Portfolio: https://barnettyang.herokuapp.com/ --- Source: https://flows.cv/barnett JSON Resume: https://flows.cv/barnett/resume.json Last updated: 2026-04-13