Ned Ruggeri.

Ned Ruggeri — Resume

Summary

Experienced software engineer with focus on data science problems at the interface of math/stats and computer science. Architected and implemented petabyte-scale big-data software. Experience with machine-trained statistical modeling.

Personal traits: creative, fast learner, passionate.

Employment

Software Engineer, Search Index Team (Jan 2012 - May 2012)
Google

Worked on big data software on Google's search indexing pipeline.

Modeling Scientist, Statistical Modeling Group (Jan 2011 - Oct 2011)
Quantcast

Quantcast is the hedge-fund of online advertising markets. Promoted to "Quant" role building statistical models. Tasked with developing Quantcast's 2nd-generation technology integrating real-time signals into our algos for high-frequency pricing updates.

Software Engineer, Data Pipeline (Mar 2010 - Jan 2011)
Quantcast

Joined as the second member of Quantcast's data pipeline team. I was responsible for re-architecting our core Hadoop MapReduce pipeline to achieve scalability and production-worthiness.

TA (Sep 2009 - Mar 2010)
University of Chicago, CS Department

TA'd undergrad and master's courses in algorithms, Java, and C++. Nominated for teaching prize.

Academic

B.A. (Honors), Mathematics; University of Chicago, 2009

Each summer, awarded stipend from department to fund undergraduate mathematics research and take graduate courses.

In addition to theoretical and applied mathematics, took coursework in analysis of algorithms, distributed computing, machine learning, probability theory and statistics.

1600 SAT score

Technical Skills

Very strong programmer. Developed large projects in Java, C, and C++. Shell and Python scripting. R. Haskell and Clojure. SQL.

Experience with Rails/JavaScript and iOS building web and mobile apps.

Distributed programming: wrote petabyte-scale Hadoop MapReduce jobs at Quantcast. Experience with MPI at college.

Very strong *NIX credentials. I'm a shell CLI guy; I'm handy with sed/grep, paste/join, xargs. I write a lot of code in emacs.

Git evangelist and hand-holder within Quantcast.

Machine Learning

Techniques I've used include:

  • Bayesian classifiers
  • Linear Regression (stochastic gradient descent)
  • Hidden Markov models (EM)
  • Latent Dirichlet allocation (MCMC)
  • Locality sensitive hashing clustering