Helping biologists automate their workflows on an affordable, open-source pipetting robot!

• We're open source so you can see all my activity over my years at Opentrons on https://github.com/opentrons/opentrons including contributions, PR reviews, and RFCs

• I was the initial developer and architect of Protocol Designer and served as technical lead as our squad was built out. Protocol Designer is a complex and highly interactive React + Redux + Typescript webapp for biologists to design and modify protocols using the Opentrons pipetting robot platform.

• I designed and maintained the JSON protocol schema we used for our protocols, a domain-specific language of atomic commands performed on the robot. I'm a strong advocate of schemas and RFCs and thoughtful communication across technical and product teams!

• Very closely collaborated with product + design. IMO it's essential to have cross-disciplinary teams when building a novel application.

• So much more!! It's a *journey* being part of a quickly-evolving startup.

* Engineer multiple React + Redux projects

* Design & build reusable private Javascript libraries shared between front-end projects

* Help engineer the build & deploy process using GitHub + CircleCI + Heroku + AWS S3

* Directly manage contractors for front-end and back-end projects

* Translate business requirements into technical solutions

* Refactor brown-field projects to be more agile and extensible

Unify disparate "siloed" data sources into a unified queryable data warehouse to be able to inform data-driven business decisions.

Teach python, pandas, scikit-learn, AWS, machine learning, natural language processing, and other tools and topics to students of Metis Data Science Bootcamp. Troubleshoot problems, debug, and guide student's approaches to their individual data science projects.

Metis is a 12 week immersive Data Science bootcamp covering Machine Learning, Statistical Inference, communication, web scraping, and web development. Tools used are centered around the scientific Python libraries: scikit-learn, statsmodels, pandas, NumPy, and SciPy.

Build data products with Flask, SQL, MongoDB, and Amazon Web Services (AWS).

See below for my slide decks of my data science projects.