Machine learning boom: greater hiring for machine learning roles in Asia-Pacific power

Asia-Pacific was the fastest growing region for machine learning hiring among power industry companies in the three months ending October.

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The number of roles in Asia-Pacific made up 28.6% of total machine learning jobs, up from 20.2% in the same quarter last year. That was followed by Europe, which saw a 6.5% year-on-year change in machine learning roles.

Which countries are seeing the most growth for machine learning roles in the power industry?

The fastest growing country was India, which saw 17.4% of all machine learning job adverts in the three months ending October last year, increasing to 26% in the three months ending October this year.

That was followed by the UK (up 2.6%), Italy (up 2.1%), and Finland (up 1.7%).

The top country for machine learning roles in the power industry is the US, which saw 37.3% of all roles in the three months ending October.

Which cities are the biggest hubs for machine learning workers in the power industry?

Some 4.7% of all power industry machine learning roles were advertised in Pune (India) in the three months ending October, more than any other city.

That was followed by Bengaluru (India) with 4.7%, San Francisco (US) with 3%, and Chennai (India) with 2.2%.


GlobalData has compiled a list of top MNCs based on revenue. Any top companies that did not have a subsidiary were removed from the list. The latest company annual reports (2019 and 2020, where available) and websites were analysed for a total of 2,188 companies.

For a subsidiary to be included, the parent company had to have a majority ownership/control in the subsidiary. Affiliates, associates, joint operations and joint ventures were included as long as the ownership criteria was met. Subsidiary information was captured at a country level. Country names were standardised. In total, 216,898 subsidiaries were captured.

Methodology and metrics

The figures are compiled by GlobalData, which tracks the number of new job postings from key companies in various sectors over time. Using textual analysis, these job advertisements are then classified thematically.

GlobalData's thematic approach to sector activity seeks to group key company information by topic to see which companies are best placed to weather the disruptions coming to their industries.

These key themes, which include machine learning, are chosen to cover "any issue that keeps a CEO awake at night".

By tracking them across job advertisements it allows us to see which companies are leading the way on specific issues and which are dragging their heels, and importantly where the market is expanding and contracting.

GlobalData’s unique Job analytics enables 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.