Took a professional risk joining a small seed-stage startup as engineer #3. Harvested Financial was the first robo-advisor to manage financial derivatives for retail investors. Built core infrastructure, engineering processes, and data security practices.

• designed and implemented a security strategy for encryption of all customer PII including SSN, address, tax filings, DOB, etc using client-side field level encryption

• developed re-usable event-driven architectures using GCP Cloud Scheduler, GCP Pub / Sub, and Compute Engine for ETLs that batch process customer data

• developed core CI / CD pipelines using GCP Cloud Build / Bitbucket and latest python testing frameworks (tox, pipenv, pytest, etc)

I learned a lot about financial derivatives, options markets, early-stage startup business needs, and building financial applications.

Developed the machine learning platform at 23andMe, which uses ML to provide predictions to customers on their risk for genetically-linked diseases. Built tools to speed up the iteration cycle for engineers and data scientists and solutions for the end-to-end ML lifecycle: training, deploying, bulk computing, serving, monitoring, and model management.

• developed automated pipelines and datastores that monitor the performance of models in production and display statistical metrics in both custom web applications and Databricks MLFlow

• scaled services for training & serving models on large genomic datasets using Elastic Container Service (ECS) and AWS Batch

• built a real-time prediction service with 200ms latency for model serving while increasing the infrastructure limit for both larger and more accurate models by 10x

I learned how to build and architect machine learning applications in AWS and tools for data scientists

I worked for the data science team at Socos, a startup that combines behavioral psychology and machine learning to make recommendations for parenting and child development.

• modeled the latent factors for neighborhoods (zipcode specific) using public information for IRS tax returns and demographic data

• developed the personalized recommender system for survey style questions that users receive everyday

• clustered similar user profiles, imputed answers to questions users haven’t answered, and leveraged the semantic relatedness of questions

• created visualizations for user engagement and profiles on personality attributes of children

I learned how to take ML into production and what’s state-of-the-art through reading papers and great mentorship.

I worked for Visa Token Services – the API for Apple Pay and Google Wallet. I prototyped a dashboard for my manager to compile # of open bugs, release stages, health of different environments, etc., across teams in one place. Using a MEAN stack, I mostly developed the front-end views & charts using Angular, but I also contributed to the Mongo ‘schema’ (although Mongo is schema-less) and created new APIs for additional features. I learned a lot about security in the payments industry.