Devo Technology, the cloud-native logging and security analytics company, announced the expansion of its SciSec threat research team with the addition of two data science experts. Dr. Kevin Zhou will serve as Vice President of Data Science and is joined by Dr. Chaz Lever, Senior Director of Security Research. Zhou and Lever bring extensive experience in data science, machine learning and AI in both academia and industry to their new roles at Devo. Their combined expertise and leadership will be central to Devo's vision to deliver what the company calls the autonomous security operations center (SOC) – complete visibility, automation, augmented analytics, and open access to community expertise and content. In his role, Dr. Kevin Zhou will lead Devo's global data science team, spearheading machine learning and AI initiatives for the company.

So what does it take to build a successful data science team? Whether you are the first "data person" at your organization or leading a team of hundreds, we know success is not based on just technology; it requires people to create a productive, effective, and collaborative data science team. Last month's webinar featured data science leaders from Caliber Home Loans, The Looma Project, Saturn Cloud, T-Mobile, and Warner Music Group to start to answer this question. You can view the recording of the webinar at Building Effective Data Science Teams. There were so many great follow-up questions that we'd like to keep this conversation going. We've also added links to an RStudio Community thread for each individual question if you'd like to continue the conversation there as well. We have paraphrased and distilled portions of the responses for brevity and narrative quality. What is a symptom that you have observed, during your time in this field, of a team being low on credibility within an organization or with stakeholders?

Byron Deeter from Bessemer went on CNBC this morning and talked about the business word of the year, growth. He made two crucial points Data Science teams need to know about. Companies are willing to spend less to get the same amount of new revenue than they were just three months ago. He brought up growth efficiency. Last year businesses were willing to spend $2 to get $1 in recurring revenue. Now it is closer to $1 spent to generate $1 in recurring revenue.

The world of data can be chaos! New technologies, tools, products and the ever-changing industry dynamics--there sure is a lot to keep up with. So, what do you do to cut through the noise? Well… one way is to follow the greatest in the world of data science and simply hang on to their every word. We created a list of people who are followed by the humans of data around the world, share their experiences and insights regularly on social media and are well connected to the community.

There is a tremendous amount of active research in making deep learning models interpretable (e.g., LIME and Layer wise Relevance Propagation). In summary, a high accuracy data science component by itself may not mean much even if it solves a pressing business need. On one extreme, it could be that the data science solution achieves high accuracy at the cost of high compute power or high turnaround time, neither of which are acceptable by the business. On the other extreme, it could be that the component that the end-user interacts with has minimal sensitivity to the errors of the data science component and thus a relatively simpler model would have sufficed the business needs. A good understanding of how the data science component fits into the overall end-to-end solution will undoubtedly help make the right design and implementation decisions.

Research in Deep Learning started as early as the 1960s, though the term itself was coined in 1986. With accurate predictions becoming the need of the hour, the amount of computing available and the massive data being collected, Deep Learning became the preferred algorithms at least over the last 5 years or so. As the complexity of problems arose, Deep Learning became the answer for problems that involved heavy datasets. A few millions of rows of supervised learning could effectively be crunched by ensemble tree based algorithms itself. However, for problems like computer vision or speech-to- text, deep learning was the answer.

Research in Deep Learning started as early as the 1960s, though the term itself was coined in 1986. With accurate predictions becoming the need of the hour, the amount of computing available and the massive data being collected, Deep Learning became the preferred algorithms at least over the last 5 years or so. As the complexity of problems arose, Deep Learning became the answer for problems that involved heavy datasets. A few millions of rows of supervised learning could effectively be crunched by ensemble tree based algorithms itself. However, for problems like computer vision or speech-to- text, deep learning was the answer.

Managing stakeholders in the world of data science projects is a tricky prospect. I have seen a lot of executives and professionals get swept up in the hype around data science without properly understanding what a full-blown project entails. And I don't say this lightly – my career has been at the very cusp of machine learning and delivery. I hold a Ph.D. in Data Science and Machine Learning from one of the best institutions in the world and have several years of experience working with some of the top industry research labs. I moved to Yodlee, a FinTech organization, in 2016 to run the data sciences product delivery division.

Operating a data science team is not something that can just be learned by watching lectures and videos on Coursera and Udemy. Don't get us wrong, they are great places to learn data science and machine learning theory with practice problems. However, they don't teach good business practices, and how to operate a data team in a business settings. Knowing algorithms, and how to use Hadoop is not enough to have an effective data team. Teams have to work with other departments, they have to maintain software, report to executives, and of course, return business value!