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Building Global AI Teams

Talent Intelligence October 12, 2018

Building Global AI Teams


The highly specialized and often misunderstood nature of AI has organizations struggling to identify the right talent to create high-impact teams. The basic premise of creating an effective AI team should revolve around recruiting individuals who are proficient in a specific area. You bring a range of uniquely skilled individuals together and allow the sum to become greater than its individual parts. This is where recruiters usually miss the bus as far as their new-age talent planning efforts are concerned.

Data scientist roles are quite fluid and have a way of fitting into every business function. Corporations are starting to make the shift from counting on business analysts and their intuition to data scientists and their cold hard data-driven insights. But this fluidity comes with a unique set of challenges along the way in terms of team structure and hierarchy. Finding the right balance or mix of data scientists and creating a formal reporting structure has been a problem plaguing startups and corporations alike.

Creating a data science team would require organizations asking themselves critical questions such as ‘how do I define their roles?’, ‘what is the formal team structure?’, ‘who do they report to?’ and what business function do they live in?’. To help understand the convoluted world of AI, Draup performed a study to understand the deeper characteristics of AI and Big Data roles and understand their impact on the global talent ecosystem.

The Data Scientist title has either expanded or has been used rather loosely and become all too vague. However, we look at some commonly accepted titles and what their roles encompass.

Data Scientists analyze raw data and turn them into actionable contextualized content.

Data Architect’s role is to capture, structure, organize, centralize, and contextualize data.

Data Engineer – These individuals lay the building blocks or infrastructure required to prepare and transform data in the organization.

Data Analysts – Their role includes gathering data, evaluating large datasets, data quality, develop analysis and reporting capabilities.

Draup’s Study on AI and Big Data

Draup conducted a comprehensive study to understand the deeper characteristics of AI and Big Data talent that can enable HR leaders in their workforce planning initiatives. We focused our analysis on top G500 R&D spenders, start-ups and service providers. We began with a corpus of around 28 million job descriptions which we narrowed down to around 1 million jobs that are most relevant as of 2018.

We identified 17 unique job roles within AI and Big Data. This is made up of 7 primary Big Data roles, 3 primary AI roles, and 7 auxiliary roles. We’ve identified these roles across G500 corporations, service providers and start-ups to get an accurate mix of mature as well as new-age roles.

Draup-Study-on-AI-Big Data

Talent Supply-Demand Analysis

The global supply of AI/Big Data talent is nowhere near meeting the prevailing demand and the imbalance is expected to go on for the next decade or so. To lay the groundwork for effective talent planning in new age skills, we performed a preliminary research which revealed that the global demand for AI/Big Data talent is close to 1.2 million jobs as of 2018. Within this, there is an unmet demand of around 500,000 and close to 300,000 of these jobs are open in the US alone.


Despite the high influx of talent in these emerging skills, there’s still a vast gap between the talent supply and prevailing demand. We’ve estimated close to 515,000 job openings in AI and Big Data. US, China, UK, and India have the largest number of job openings in these skills with over 60% of these openings in the US alone. Globally, we’re set to see a huge boost in the number of job openings over the next few years. Job creation for AI and Big Data roles will reach close to 1 million by 2021 with an average CAGR of 23%.


Understanding the Data Scientist Role Progression

Organizations hire data scientists, data analysts and also data engineers to handle their complex data requirements. The role of the data scientist has evolved over the last decade and has funneled through from parallel roles such as statistician simply because of how these roles are intertwined. Draup analyzed the data scientist profile across the top 10 tech giants. We found that the business analyst and algorithms engineer roles are the key roles which have progressed into the modern-day data scientist role. Some other common progressions are illustrated below:


Concentration of Talent Across Verticals

From our preliminary analysis, we’ve identified the presence of installed talent across G500 corporations which are consolidated in tier-1 locations. They account for nearly 44% of the total employed Big Data and AI talent pool. Over 60% of the demand is distributed across enterprise software, consumer electronics and banking and financial services. Emerging global locations are also showing high talent scalability potential thanks to governments taking cognizance of AI and Big Data and increasing spending to strengthen infrastructure and incubating the ecosystem.


US, China, Israel, and India have a high concentration of AI and Big Data talent. The talent in China, Israel, and the US is predominantly employed by US-based tech giants while India has most of its AI/BD talent with service providers such as IBM, TCS, and Infosys. This is contrary to trends in Canada and the UK where there is a large presence of AI and Big Data talent at start-ups and niche sized mid-companies.

Global Hotspots and Emerging Hotbeds


There’s a high concentration of AI and Big Data talent across hotbeds such as Boston, Seattle, Atlanta, Tel Aviv, Bangalore and Shanghai. The real talking point, however, should be the emergence of talent in Tier-2 locations. Several financial, socio-economic and bureaucratic factors have led to talent migration from Tier-1 to more conducive Tier-2 locations. Draup has identified over 130 emerging hotbeds for AI and Big Data skills globally. Over 20% of the total talent is currently located in Tier-2 locations and is only set to grow further. Our initial projections also indicate the presence of over 1mn machine learning developers across 37 countries by 2030.

University Talent Supply Assessment

talent recruitment

Draup’s talent module analyzed universities in the US to identify the most sought-after institutions, and key courses in Artificial Intelligence, Machine Learning and Analytics. Stanford University has the maximum number of ‘Center of Excellence’ collaborations with tech companies. Carnegie Mellon University has emerged as a hotspot for AI and Big Data talent and has the greatest number of Machine Learning / Big Data courses and publications. CMU, UCLA, MIT and Stanford offer the most advanced courses such as Computational Biology, Genetic Algorithm and Cognitive Modelling Robotics. CMU has also collaborated with giants like Apple, Google, and Amazon for research in the field of AI, Robotics, and Deep Learning.

Geographic Analysis of AI/Big Data Talent

US – The US has the largest concentration of AI and Big Data talent. Over 65% of the US talent is spread across Seattle and the Bay Area with Data Science being the most employed role across top firms. Nearly every engineering priority is focused on building cross-industry AI platforms.

  • Seattle – Second largest talent hotspot in the US with majority of the talent in data management and data science roles employed across G500 corporations.
  • Boston – High consolidation of AI and Big Data talent across startups in healthcare and retail industries majorly employed from Tier-2 Universities.
  • Austin – High availability of niche talent employed across healthcare and retail.

China – Chinese tech giants Baidu, Tencent, and Alibaba have over 2,000 AI/BD engineers spread across tier-1 locations such as Shanghai, Beijing, and Shenzhen. There are 20+ universities that offer PhD programmes in AI/Big Data and over 30 universities offering undergrad courses in AI and Machine Learning. Overall, China has a very mature AI/ML talent ecosystem with Chinese technical universities producing ~80,000 fresh talent annually.

Israel – All eyes are definitely on Israel as they grow towards becoming a global AI and Big Data powerhouse. A big portion of the talent in Israel is spread across start-ups focused on industry-specific applications such as cybersecurity and healthcare. Israel has over 400 startups in the space of cyber security which employs a big portion of the available talent.

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