Introduced concepts of product similarity and product compliment based on outfits using the appropriate machine learning algorithm, giving IMVU to have the ability to recommend "This hat is like these hats", "This hat goes with these shoes", and "Here are dresses that go with this hat and those shoes" covering 50% of 25 million products.

Introduced Spark, configured it to work over a large hive cluster, and tutored data team in scala / spark. A 10x performance boost aside from a host of other benefits.

Grew Franz, a high performance multi-threaded event ingestion application written in Haskell to ingest events and feed them to Kafka with low latency.

Provided guidance for the transition to typed-functional programming in Scala from an originally Java code base.

Demonstrated use of Gatling as a library rather than an application to unify load and functional testing, within arbitrary JVM frameworks.

Added instant account verification to the backend banking services.

Designed and implemented prototype algorithm to extract brand names from product names, and algorithms to classify product mentions in text selections, which were then implemented in the production application. These help cut down processing time as well as help eliminate false positives.

Emphasized importance of ground truth as a model metric, and used MTurk to generate ground truth in order to test algorithm / model improvement iterations.

Constructed a tweet-music metadata matching algorithm. It used the music corpus to identify music stop words and music word importance, a tweet parser which extracted and grouped important tweet words, and a relatively small number of parameters so that it wouldn't require a great deal of ground truth to train. It could eliminate almost all false positives while generating only a small number of false negatives.

Implemented a monadic F# evaluator for typed dataframes allowing user specified field and row transformations. Multiple user transformations were fused together so the dataframe modification was performed in a single pass.

Invented a robust (that is, it worked well on a wide variety of distributions--not just normal distributions) extreme value anomaly detector algorithm.

Wrote a probabilistic column type detector which worked reliably with only a small sample of the data, even with the presence of out of bound values.

Extended the secure Azure blob to Amazon EBS file migration infrastructure to work in both directions as well as support both user- and project-centric files via typed queue messages.