Building a foundation for scaling data science, such as enabling Spark job submission and hosting JupyterLab instances on DataProc clusters.

Collaborating with air quality scientists to build a data quality/data visualization engine to detect anomalous behavior in our data.

Leading an effort to reorganize our data warehouse to align further with industry and academic air quality sensor data research.

Mentoring engineers and supporting their efforts on large, impactful projects.

Modernizing our use of Google Cloud Platform assets, using Terraform, and reinforcing our security posture across multiple projects.

Enhanced antibody physical/chemical properties calculations, such as new methods and configurability for isoelectric point and extinction coefficient.

Participated in rigorous code review process, maintained organized branching standards, and stuck to an agile methodology.

Contributed to a general .NET architecture for other developers to use in their client specific projects.

Use .NET Framework, TypeScript, KnockoutJS, Bootstrap, Postgres on a daily basis.

Migrated new and legacy applications onto Azure services, such as Azure App Service and SQL Azure.

Integrated 3rd party supplied systems and data feeds.

Architected solutions to a range of problems from styling minutia to large scale app refactor/redesign.

Used .NET Core MVC, C#, TypeScript, Dapper, Bootstrap, Kendo, SQL Server on a daily basis.

Created a word embeddings model, using Gensim, trained on investment management agreements to identify most similar text from past agreements.

Performed an exploratory data analysis on environment, social, and governance (ESG) data and presented to the head of sustainability research.

Developed an NLP model drift monitoring application, hosted on Docker containers, and deployed through custom-built DevOps pipelines using Azure resources.

Engineered a data-driven application for identifying real estate opportunities for the alternative investments team.