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Dare to be a Data Scientist

September 26, 2016

Boston is at the forefront of a revolution in data science. We encourage the innovative mindset that doesn’t shy away from the most complex problems, identifies those unasked questions, and then employs science and art in trying to answer them. With local hubs in Lexington, Westborough and now at the heart of innovation in downtown Boston from the Cambridge Innovation Center, we are proud to be part of this community, challenging what’s possible with data science.

With a team of over 500 data scientists across our different locations, this mindset is ingrained in our culture. We not only solve problems for our clients, but take part in hackathons, attend local meetups, and work with partners to convene communities for broader purpose. For example, the 2016 Data Science Bowl presented by Booz Allen Hamilton and Kaggle, brought together data scientists around the world to help transform heart disease diagnosis.

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Data scientists like those involved in the Data Science Bowl are more than just the statistician or researcher. There are options and choices in the field that barely even existed five years ago the way they do today. So much of the work happening now requires the understanding and blending of dissimilar formats or inputs in the data to tease out actionable predictions. A sampling of our own data scientists in the firm will show individuals with backgrounds in Ocean Physics, Finance, Economics, Astrophysics, Medicine, Philosophy or Forestry.

As a Chief Scientist at Booz Allen, I’m often asked what it takes to become a great data scientist. Data science is such a fascinating field, because merely excelling in statistics or the newest querying language doesn’t equate with being a great data scientist. In fact, the cutting-edge medical advancement in heart disease diagnoses discovered at the Data Science Bowl was not created by a biologist or computer programmer, but rather two hedge fund analysts with no medical experience.

This is not to say that a background in mathematics or programming isn’t helpful. Data scientists need a strong foundation in statistical analysis and languages, such as R. They must master algorithmic programming and machine learning, navigate through data structures for information retrieval, and remain comfortable with a little ambiguity.

A great data scientist is a problem solver who uses intuition, creativity, and a deep understanding of the situation to identify opportunities in the data. Great data scientists are audacious and tenacious, asking questions of the data that nobody else is willing to ask. They must be risk takers, trusting that their skills and analyses will tease out new solutions to previously unsolvable challenges. Most importantly, great data scientists understand there’s always more to learn and are willing to take on new and meaningful challenges that dare to advance the art of data science.

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Inside the Booz Allen Hamilton offices in Boston.


As a long-time Bostonian and analytics practitioner, I’m proud to be part of a community that reflects our collective entrepreneurial culture, exemplified by the hundreds of tech companies and 50-plus local universities graduating over 5,000 students from data science backgrounds. We’re a community where you can find a meetup or lecture on topics ranging from distributed databases and cloud computing to social hack-a-thons on any given day. I have witnessed the evolution of the field over the last decade and am excited for the many possibilities it holds in a world where technological advances married with new schools of thinking solve new problems every day. We, at Booz Allen, are proud to be part of this future and look forward to seeing what the next generation will create. I invite you to join us on the journey.

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Alex Cosmas, Chief Scientist and Principal at Booz Allen Hamilton

Alex is the managing Principal of Booz Allen Hamilton’s Boston Office and Chief Scientist in the firm’s Analytics practice.  He is a recognized expert in the use of predictive and probabilistic models to perform both deductive and inductive reasoning from large datasets, and leads a capability team delivering advanced analytics across the public and private sectors. Alex has consulted for Fortune 100's both domestically and internationally in the areas of demand modeling, consumer choice, network modeling, revenue management and pricing. He is a member of the Institute for Operations Research and Management Science (INFORMS) and the Airline Group of the International Federation of Operations Research Societies (AGIFORS). He earned a B.S. in Applied Physics from Columbia University’s School of Engineering and Applied Science, an M.S. in Technology & Policy and an M.S. in Aerospace Engineering, both from the Massachusetts Institute of Technology.