By Sebastian Gutierrez
Info Scientists at paintings is a suite of interviews with 16 of the world's so much influential and leading edge facts scientists from around the spectrum of this scorching new occupation. "Data scientist is the sexiest task within the twenty first century," in response to the Harvard enterprise overview. by means of 2018, the USA will event a scarcity of 190,000 expert information scientists, in accordance with a McKinsey document. each one of those information scientists stocks how she or he tailors the torrent-taming options of massive info, information visualization, seek, and information to precise jobs through dint of ingenuity, mind's eye, persistence, and fervour. facts Scientists at paintings components the curtain at the interviewees' earliest info tasks, how they turned info scientists, their discoveries and surprises in operating with facts, their options at the prior, current, and way forward for the career, their reviews of staff collaboration inside their companies, and the insights they've got received as they get their fingers soiled refining mountains of uncooked facts into items of industrial, clinical, and academic worth for his or her companies and consumers.
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Additional resources for Data Scientists at Work
Working with those teams is always a big part of our team’s work. We also spend time studying model results and iterating on models. This includes the modeling piece as well as the tactical piece of whether a model is working. And if it’s not working, what are some of the ideas that are around that we could try differently? So studying results here as well. info 27 28 Chapter 2 | Caitlin Smallwood, Netflix Similarly, we spend time studying results of experiments that we do across various topics.
I made all of the standard rookie management mistakes in learning how to manage, and I made hiring mistakes and had to live with those. After having to figure out how to deal with all of those things, you kind of learn. Gutierrez: What do you look for in people when in hiring? Smallwood: I would say the top things are hunger and insatiable curiosity. You imagine a data set and you salivate at just thinking about that data set. Those are the top qualities, because people who always want to dig more, mine the data, and learn new things from the data are the people who are happiest in this kind of job.
After his postdoc work in Geoff Hinton’s group at the University of Toronto developing the theory of artificial neural networks with back-propagation, he joined AT&T Bell Labs, where he later became the head of the Image Processing Research Department. LeCun then worked briefly as a Fellow of the NEC Research Institute in Princeton before joining NYU in 2003. Over his career to date, he has published more than 180 technical papers and book chapters on machine learning, computer vision, handwriting recognition, image processing and compression, and neural networks.