data manufacturing architect
Data business companies offer even more specialized roles. For example, Bloomberg recently hired a new role as a data manufacturing architect for its CTO data science team.
Data Manufacturing Architects help Bloomberg create high-quality structured data for its financial services customers, including more than 325,000 Bloomberg Terminal clients. Gideon Mann, director of data science at Bloomberg's Office of the Chief Technology Officer, said data comes from unstructured and noisy sources.
“These numbers have to be accurate and accurate, exceeding most industry and academic standards,” he says.
The data manufacturing architect works as a deep domain specialist in Bloomberg's global data division, he says. Bloomberg is currently hiring for a number of his other AI professional roles, including AI Research Scientist, AI Quantitative Research Scientist, Human Computing Architect, Senior ML Engineer in Media Data Science, and Senior Software Engineer in Distributed Systems. I am.
These roles require experience in AL, ML, natural language processing, information retrieval, and quantitative finance, with expertise in programming languages such as Python, Java, and C++, according to Anju Kambadur, head of AI engineering at Bloomberg. It requires knowledge. But communication, collaboration and product development skills are also important, he added. “Especially the ability to work and communicate across organizational boundaries and disciplines.”
AI quality assurance manager
As cutting-edge companies explore how to allocate responsibility for early AI practices, additional AI jobs are emerging to meet their needs. Some of these jobs do not yet exist on the job market, and most do not have a standardized curriculum or typical career development path.
For example, consider the new role of AI Quality Assurance Manager. While this can be seen as an evolution from traditional software quality assurance work, quality assurance for AI projects is dramatically different. For example, a company may choose the wrong algorithm for the project at hand, but the code itself is rarely the problem. More importantly, incomplete, outdated, or biased training data sets.
Biased data is a particularly troubling problem and can lead to not only negative outcomes but also regulatory repercussions, negative publicity, fines, lawsuits, and more.
“No one really understands how bias gets into data or how to remove bias from data,” said John, chief data scientist at Edgewise Networks, which was recently acquired by Zscaler. says one John O'Neil. “This is an active area of research. As far as I know, there's no place where you can say there are rules, and if you follow the rules, you'll be fine.”
citizen data scientist
According to Gartner, AI power users will fill the data scientist talent gap by 2024. “Citizen data scientists,” as Gartner calls them, will be able to perform AI-related tasks as the tools needed to deploy advanced analytics, machine learning, and artificial intelligence become increasingly accessible.
However, do not search for this as a job title. Instead, experience with “citizen data scientist” tools such as Auto ML will be part of the job description for various roles.
“Traditional data scientists are expensive to hire, scale, and train,” says Ryohei Fujimaki, CEO and founder of AI platform company DotData.
However, according to a March study from IDC, approximately 28% of AI and machine learning initiatives fail, largely due to a lack of skills. “Lack of staff with the necessary expertise has been reported as one of the main reasons for failure,” says Jyoti of he IDC.
That means there is pent-up demand to reskill employees in AI and ML, she says.
And the need for “citizen data scientists'' is increasing, says DotData's Fujimaki.
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