North America has extended its dominance of ‘machine learning’ hirings among beverage companies, according to recent research.
In the three months to the end of December, the number of positions advertised in the region accounted for 51.8% of all machine learning vacancies – a slight lift on the 49.5% level in the same quarter a year earlier. North America was followed by South & Central America, which saw a 2.1 year-on-year percentage point lift in machine learning vacancies.
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By GlobalDataThe figures are compiled by GlobalData, which tracks the number of new job postings from key companies in various industries. Using textual analysis, these job ads are then classified thematically to gauge which companies are best placed to weather future industry disruptions.
The research is designed to indicate which companies are leading the way on specific issues as well as where the market is expanding and contracting.
Which countries are seeing the most growth for machine learning job ads in the drinks industry?
The fastest-growing country was India, which accounted for 13.3% of all machine learning adverts in Q4. Twelve months earlier, the country's proportion was 5.5%.
India was followed by the US (rising 2.9 percentage points), Brazil (up 2.1), and Switzerland (inching up 0.3).
In beverages, the US was the largest market for machine learning roles, accounting for 48.8% of all the country's vacancies in the industry over the three months.
Which cities are the biggest hubs for machine learning workers in the drinks industry?
The leading cities were Plano in the US and Hyderabad in India, each with 12.7% of beverages' machine learning vacancies in the quarter. The pair were followed by Atlanta (7.8%) and New York (6.6%).
Methodology: GlobalData’s ‘Job Analytics’ enables an understanding of hiring trends, strategies and predictive signals across sectors, themes, companies and geographies. Intelligent web crawlers capture data from publicly available sources. Key parameters include active, posted and closed jobs, posting duration, experience, seniority level, educational qualifications and skills.
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