My nine-month open-source "masters" in machine learning and statistics — a full-time, self-curated schedule of textbooks, MOOCs, publishing code to my GitHub and writing to this blog — is soon ending. I'm now starting my search for an impossibly awesome "what's next."

What I bring

I bring abundant experience in: machine learning, Bayesian statistics and probabilistic programming, backend engineering, visualization and making complex ideas easy to understand.

As such, within your organization, I could immediately bring value to: ETL infrastructure, experimental design and execution, internal and external data products, i.e. algorithm design and deployment, and data evangelism efforts.

I'm looking for

  • A highly technical data science role — equal parts prototyping mathematical models and deploying them to production.
  • Excellent, direct technical mentorship, and the opportunity to mentor others.
  • A medium-to-large-sized company with a well-established data science team. Alternatively, a smaller company with machine learning at the core of its business that, necessarily, has made significant investments in data science infrastructure and personnel.
  • Large quantities of unstructured data that do not fit in RAM, ideally.

Work samples

  • Minimizing the Negative Log-Likelihood, in English: Statistical underpinnings of the machine learning models we know and love. A walk through random variables, entropy, exponential family distributions, generalized linear models, maximum likelihood estimation, cross entropy, KL-divergence, maximum a posteriori estimation and going "fully Bayesian."
  • Neurally Embedded Emojis: Convolutional variational autoencoders for emoji generation and Siamese text-question-emoji-answer models. Keras, bidirectional LSTMs and snarky tweets @united within.
  • Random Effects Neural Networks in Edward and Keras: Coupling nimble probabilistic models with neural architectures in Edward and Keras: "what worked and what didn't," a conceptual overview of random effects, and directions for further exploration.
  • Further Exploring Common Probabilistic Models: Exploring generative vs. discriminative models, and sampling and variational methods for approximate inference through the lens of Bayes' theorem.
  • Dotify: A well-tested web application that recommends songs via "country arithmetic" and hand-rolled Implicit Matrix Factorization. Built with Flask, React, Webpack, PostgreSQL, Heroku and Docker.

In three years

Management, I think. I'm a strong communicator, and enjoy teaching things to anyone that will listen.


NYC/SF, primarily.

About me

  • I once rode a bicycle 7,614.5 kilometers from Istanbul, Turkey to Bishkek, Kyrgyzstan.
  • I once taught a university Spanish course, in French, in Guinea-Conakry.
  • Mathematics and code keep me smiling from ear to ear.

For more, please see my full bio and résumé. In addition, social links can be found below.