Ryan Wallace
Boston, MA | ryan@ryancswallace.dev | 617-852-9239
github.com/ryancswallace | ryancswallace.dev | linkedin.com/in/ryancswallace
Education
\bullet  Harvard University | Cambridge, MA
A.B. in Computer Science, cum laude; secondary concentration in Statistics; GPA: 3.8. | May 2018
\endash  Senior Research: “Modeling Behavior of Networked Agents with Dynamic Opinions”
\endash  Computing Coursework: Algorithms and Data Structures; Big Data Systems; Artificial Intelligence; Networks, Economics and Computation; Abstraction and Design in Computation.
\endash  Statistics Coursework: Real Analysis; Probability; Inferential Statistics; Time Series and Prediction; Generalized Linear Models; Econometrics.
Experience
\bullet  Federal Reserve Bank of Boston | Boston, MA
Lead Data Scientist, Research | January 2023 – February 2026
\endash  Designed the architecture and coordinated implementation of an AWS data lakehouse supporting terabyte-scale research datasets and reproducible analytical workflows across the Federal Reserve System.
\endash  Built Python/Spark tooling that accelerated terabyte-scale analysis, standardized researcher workflows, and automated administrative operations.
\endash  Created and led an internal technical talk series to promote best practices in data science, machine learning, and software engineering.
\bullet  Senior Data Scientist, Research | December 2021 – January 2023
\endash  Architected and developed a data lake using Spark Streaming for ELT and Airflow for job orchestration.
\endash  Categorized debit card transactions on multi-terabyte datasets using text clustering and classification techniques.
\bullet  Data Scientist, Research | December 2020 – December 2021
\endash  Partnered with economists to produce empirical reports on consumer spending response to the CARES Act. Developed a distributed lag regression model and robustness checks.
\endash  Modeled expected effectiveness of the Main Street Lending Program (MSLP) using Fed warehouse data, SQL, scikit-learn, and Plotly.
\bullet  DevOps Engineer, Research | December 2019 – December 2020
\endash  Completed migration of compute, database, and web service infrastructure to AWS using IaC under CI/CD.
\endash  Created data pipelines to continuously update datasets scraped from public sites and retrieved from REST APIs.
\bullet  Harvard Data Ventures | Cambridge, MA
President | December 2017 – September 2018
\endash  Provided technical and project management guidance on data science projects for Boston startups.
\endash  Led training of fellows in weekly coding workshops, data science seminars, and an industry sponsorship program.
\bullet  Fellow | September 2015 – December 2017
\endash  Refined project specifications, developed models, completed analyses and simulations, and presented results regularly to clients’ technical and business teams.
\bullet  Arrowstreet Capital | Boston, MA
Research Intern | June 2017 – August 2017
\endash  Implemented and deployed to production an equity risk analysis module based on a newly published model. Used Numba to achieve a 5-fold execution speedup over baseline.
\endash  Applied Bayesian regression and time-series methods to decompose returns and support portfolio risk analysis.
\bullet  BNY Mellon | New York, NY
Software Engineering Intern | June 2016 – August 2016
\endash  Developed a tool to predict the likelihood of sales conversions using an ensemble model. Enabled BNY’s sales team to sort and filter leads by estimated potential.
\endash  Implemented a Salesforce client in Apex to consume and process REST API data from BNY’s proprietary sales platform.
Skills
Languages: Python; SQL; Go; Bash; C; OCaml.
Data & ML Platforms: Spark; Airflow; AWS data lakehouse architecture; OLAP systems; stream processing; relational and key-value databases.
Modeling & Applied ML: Generalized linear models; Bayesian regression; time-series analysis; text clustering and classification; search and matching; model explanation; LLM application development.
ML Ops & Infrastructure: Model serving with Ollama and vLLM; Docker; Terraform; Ansible; Linux (RHEL, Alma); CI/CD; REST APIs; system administration automation; Git.
Observability: Prometheus; Grafana; CloudWatch.
Engineering Practices: Object-oriented and functional programming; agile development; production data tooling; technical project management.