Genetha Anne Gray | Analytics Research Scientist
Department: Data Center Group
Education: B.S. Mathematics, 1993, University of CA. Davis; M.A. Mathematics, 1997, California State University, Sacramento; M.A. Computational and Applied Math, 2001, Rice University; Ph.D. Computational and Applied Math, 2002, Rice University
Career stage: Mid—24 years post Bachelor’s
What She Does
Genetha is part of an artificial intelligence (AI) solutions group that is studying problems related to autonomous vehicles and other data-rich Internet-of-Things (IoT) devices—internet-connected objects that are able to collect and exchange data using embedded sensors. Her team is working to create a platform for the ingestion, labeling, assessment, and modeling of the 4000 TB of heterogeneous data produced by each autonomous vehicle each day. In a typical day, Genetha may have a meeting or two and may attend a talk or training (usually virtual). The rest of her time is spent reading, writing, and working with code.
Necessary Job Skills
Applied math is the basis for all the work Genetha does. Her work is based on robust theory, She needs to understand why algorithms fail or succeed.
Pros and Cons of Her Job
The fact that data science, machine learning, deep learning, and AI have become a hot field is both a blessing and a curse, according to Genetha. She appreciates the recognition of the work that she does and the investments that companies are willing to make to move the field ahead. She also appreciates all the sharing of findings from academia and industry and the new focus on AI at many conferences. The downside is that, because it is such a hot area, there are people pushing to get into the field who do not have the necessary mathematical, statistical, or computer science basics needed to be successful.
Intel is an international corporation with offices in more than 50 countries, therefore, the infrastructure is in place to attend meetings and talks from your laptop. Intel also provides transportation between its main sites, which allows Genetha to live in the town where her husband works and their son goes to school, but still easily have regular in-person meetings with her manager and teammates at other sites.
Genetha started out studying pure mathematics but then switched to applied math as she became interested in the applications of math to biology. After grad school, she wasn’t sure if she wanted to enter an academic or corporate career and opted to take a post-doc position at a national lab where she could both work on large projects with multi-disciplinary groups but also work with students. It also gave her the chance to publish and present results at conferences. Genetha was offered a regular position on the technical staff and ended up staying for over 10 years.
The first step of my career was well thought out. After that, the steps I took were a combination of need and opportunity. Changing jobs is not easy—even when it is a positive change—and I never would have planned to do it twice in six months, but it was the right thing for me at the time.
When the government shut down in 2014 and many projects were cut, she left Sandia and went to a small software company. After one week, she realized that it was not a good fit. Intel was looking to hire a data scientist for their HR team, and despite having no experience working in people analytics, Genetha decided that it sounded interesting and made a second job change just months later.
Career Expectations and Advice
“To enter data science or AI or a related field, make sure your math, stats and CS skills are strong—You will need them to be successful, and they may help you stand out from the crowd.”
Study what interests you most, not what you think is the hot area. Things change quickly and you never know what will be the next big thing or where your skills will end up taking you.
In grad school, you will probably be told the importance of networking, but you might not believe it until you get started in your career. You will be surprised by how often you come into contact with your grad school peers, friends of friends, or people that you casually meet at conferences. They can help you advance in your career and give you opportunities to collaborate on interesting projects, attend workshops and conferences, give talks, bring in new teammates, and find interesting new career options.
According to salary.com, the salary of a data scientist ranges from $105,000 to $135,000, depending on geographical location. There are often large yearly bonuses or lucrative stock options offered by companies in competition for the best talent.
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