# Ryan Wallace

Boston, MA | [ryan@ryancswallace.dev](mailto:ryan@ryancswallace.dev) | 617-852-9239

[github.com/ryancswallace](https://github.com/ryancswallace) | [ryancswallace.dev](https://ryancswallace.dev) | [linkedin.com/in/ryancswallace](https://linkedin.com/in/ryancswallace)

## Education

- **Harvard University** | Cambridge, MA

  - *A.B. in Computer Science, cum laude; secondary concentration in Statistics; GPA: 3.8.* | *May 2018*
    - *Senior Research*: “Modeling Behavior of Networked Agents with Dynamic Opinions”
    - *Computing Coursework*: Algorithms and Data Structures; Big Data Systems; Artificial Intelligence; Networks, Economics and Computation; Abstraction and Design in Computation.
    - *Statistics Coursework*: Real Analysis; Probability; Inferential Statistics; Time Series and Prediction; Generalized Linear Models; Econometrics.

## Experience

- **Federal Reserve Bank of Boston** | Boston, MA

  - *Lead Data Scientist, Research* | *January 2023 – February 2026*
    - 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.
    - Built Python/Spark tooling that accelerated terabyte-scale analysis, standardized researcher workflows, and automated administrative operations.
    - Created and led an internal technical talk series to promote best practices in data science, machine learning, and software engineering.
  - *Senior Data Scientist, Research* | *December 2021 – January 2023*
    - Architected and developed a data lake using Spark Streaming for ELT and Airflow for job orchestration.
    - Categorized debit card transactions on multi-terabyte datasets using text clustering and classification techniques.
  - *Data Scientist, Research* | *December 2020 – December 2021*
    - Partnered with economists to produce empirical reports on consumer spending response to the CARES Act. Developed a distributed lag regression model and robustness checks.
    - Modeled expected effectiveness of the Main Street Lending Program (MSLP) using Fed warehouse data, SQL, scikit-learn, and Plotly.
  - *DevOps Engineer, Research* | *December 2019 – December 2020*
    - Completed migration of compute, database, and web service infrastructure to AWS using IaC under CI/CD.
    - Created data pipelines to continuously update datasets scraped from public sites and retrieved from REST APIs.

- **Harvard Data Ventures** | Cambridge, MA

  - *President* | *December 2017 – September 2018*
    - Provided technical and project management guidance on data science projects for Boston startups.
    - Led training of fellows in weekly coding workshops, data science seminars, and an industry sponsorship program.
  - *Fellow* | *September 2015 – December 2017*
    - Refined project specifications, developed models, completed analyses and simulations, and presented results regularly to clients’ technical and business teams.

- **Arrowstreet Capital** | Boston, MA

  - *Research Intern* | *June 2017 – August 2017*
    - 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.
    - Applied Bayesian regression and time-series methods to decompose returns and support portfolio risk analysis.

- **BNY Mellon** | New York, NY

  - *Software Engineering Intern* | *June 2016 – August 2016*
    - 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.
    - 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.