1. For Linguists who Want to be Linguists, not Computer Scientists Launching a Career in Tech Jeff Lilly, Research Scientist at Amazon

2. Background MA in Linguistics from UMD College Park, 2000 Who I’ve worked for:  3 start-ups, 2 defense contractors, 1 large speech company (Nuance), and Amazon What I’ve worked on:  Search engines  Text understanding & analysis  Information extraction  Dictation software  Lexical databases of various kinds  Speech technology  Multiple patent applications Jeff Lilly, Research Scientist at Amazon

3. The Foolproof Plan Getting into tech means being the right person in the right place at the right time  The right person: has the skills for the job  The right place (metaphorically): located strategically on the social and professional networks of the industry  The right time: exactly when the company needs you So it’s a simple 3-step plan: 1. Become the right person 2. Get to the right place 3. Wait there, shouting and waving occasionally, until the right time comes Jeff Lilly, Research Scientist at Amazon

4. A Dearth There are not enough linguists in the tech industry. We’re needed.  Computer scientists and systems engineers are building systems that work, oftentimes with almost no input from linguists  But they could build things faster, better, cheaper with our help  The speech industry is swarming with brilliant computer scientists who don’t know what an allophone is  Military intelligence analysis systems are built by people who have never heard of the Binding Theory of coreference But too many of us can’t code.  Half a dozen excellent linguists with PhDs applied to work at our lab at Amazon in the past year. All were turned away because of inadequate coding ability. This should change.  Linguists want to be linguists, not computer scientists. But if you can pick up some crucial skills, you can open doors in the tech industry AND make yourself a better linguist. Jeff Lilly, Research Scientist at Amazon

5. Coding Fundamentals Computer Science Basics:  Algorithms: searching, sorting, hash tables…  Object oriented design principles  Mathematical libraries  Performance / speed / memory tradeoffs  You can get a lot of this from a few courses in Java or C++ Scripting & Unix Environment  Python, Perl  Bash  Free online courses available for these Jeff Lilly, Research Scientist at Amazon

6. Serious Coding Corpus Processing & Analysis  NLTK (Natural Language Toolkit – Python) – tokenization, chunking, etc.  Linguistic Data Consortium  WordNet, FrameNet, Google Ngrams, UPenn’s Eigenwords, etc. Machine Learning  Andrew Ng’s Stanford online course: highly recommended as a broad survey of techniques applicable across all kinds of problems  Find other coursework specific to what you want to focus on  Phonetics, phonology: speech recognition and production (HMM’s, GMM’s, DNN’s, ngram LM’s)  Syntax, semantics: information extraction, dialogue systems, machine translation (CRF’s, statistical parsing) Jeff Lilly, Research Scientist at Amazon

7. Get Experience Don’t just take courses.  Use this in your research. Aim to publish work that shows you can code, you can process and analyze corpora, and you can build machine learning systems. Examples of Using Machine Learning for Linguistics:  Build a corpus of Pittsburgh English, and build a system to compare it to a General American corpus to find distinctive dialect features  Break up a corpus of spoken language by dialect, register, etc.  Use the eigenword corpus and FrameNet to explore the relationship between verb distribution and argument structure Jeff Lilly, Research Scientist at Amazon

8. The Right Place, the Right Time Meet People  I’m terrible at this, but it’s a learnable skill; been hired 5 times, and 3 times it was because of a network connection  There are tools that help: LinkedIn is a big deal – get on it!  It has revolutionized tech recruiting in just the last few years  Conferences continue to be essential Other Notes  Linguist list has many jobs, but lots of potential employers haven’t heard of it  Job search sites (monster, careerbuilder, indeed) are good too, particularly for defense industry  Job market is always in flux; if you don’t like it now, wait a month.  Companies don’t always want people like us, but when they do, they want us now and they want us bad  As in almost all industries, it’s easier to find a job if you’re willing to move Jeff Lilly, Research Scientist at Amazon

9. A Case Study For three years we turned down excellent linguists with PhDs but lackluster coding ability A few weeks ago, we hired an excellent linguist with an MA and solid coding skills  Halfway through her Master’s program in computational linguistics, she came and interned with us for a few months  Pluses: Java, OO, scripting, algorithms, corpus work (nothing spectacular, but solid)  Minuses: no machine learning  She had a lot to learn still, but she worked hard and we gave her a job offer We need more people like this Jeff Lilly, Research Scientist at Amazon

10. Wrap-Up The Right Person:  Knowledge of coding fundamentals  Experience with machine learning and corpus analysis  These skills help you in linguistics AND the tech industry The Right Place & Time:  Networking, patience, persistence  Start now:  www.linkedin.com/pub/jeff-lilly/3/932/308/ www.linkedin.com/pub/jeff-lilly/3/932/308/  Thomas Schaaf (local to Pgh): www.linkedin.com/pub/thomas-schaaf/0/a77/34b www.linkedin.com/pub/thomas-schaaf/0/a77/34b  Amazon is hoping to open an office in town if there is enough of a local talent pool Jeff Lilly, Research Scientist at Amazon