choose a skill cluster
skills & motivations.
This section provides a breakdown of the inherent skills, learned skills and motivations/aspirations for each skill cluster. Inherent skills and motivations/aspirations combined are commonly defined as “soft skills” in the market.
inherent skills
- 1. curiosity
- 2. analytical thinking
- 3. mathematical ability
- 4. attention to detail
- 5. creativity
- 6. persistence
- 7. interest in technology
learned skills
- 1. data visualization
- 2. data analysis
- 3. data infrastructure and tools
- 4. machine learning
- 5. domain-specific analytics
- 6. data wrangling
- 7. predictive modeling
- 8. Big Data technologies
motivations/aspirations
- 1. intellectual curiosity
- 2. problem-solving
- 3. impact on decision-making
- 4. innovation and creativity
- 5. collaboration and interdisciplinary work
- 6. financial rewards
- 7. career growth opportunities
- 8. contribution to social good
- 9. technological advancement
- 10. recognition and achievement
- 11. work-life balance
- 12. alignment with organizational goals and values
- 13. entrepreneurial opportunities
what it shows
The chart here illustrates the sub-level of learned skills required for data science and analytics in each of the 24 markets researched. The findings presented here are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks and job boards, as well as career sites.
need to know
- Data science and analytics is an extremely broad category with a variety of tools and programming languages that can be applied across different tech clusters, making it also one of the most mobile talent pools.
- Experience with Python, expert Excel and visualization skills, and the ability to master Big Data technologies are among the most frequently occurring skills in this cluster.
skills supply.
what it shows
Skills supply data indicates the total number of individuals who have the skills required for data science and analytics in each of the 24 markets researched. These figures are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks and job boards, as well as career sites.
Use the chart to understand the availability of skills (“supply map”), availability of sub-skills (“skill type”), talent with recent job search activity (“active talent”), as well as the share of talent who prefer permanent or contract work (“preferred employment type”).
The AI filter can help you understand the ways in which talent supply has been impacted by the growth of AI.
need to know
- Supply for the data science and analytics cluster is one of the fastest growing across all clusters, on par with AI and automation. The cluster also had the most growth in new talent (5%) and a significant share of experienced talent acquiring the skills to move into the data science field.
- The cluster has the second-highest level of mobility, just after AI and automation, and the second-highest share of talent actively looking for new opportunities, after marketing, content and advertising.
- Data science and analytics has seen the fastest adoption of AI skills across the board, with almost one-third of the overall talent pool adopting some level of AI skills.
skills demand.
what it shows
Skills demand data indicates job postings that require data science and analytics skills in each of the 24 markets we researched. These figures are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks and job boards, and career sites.
See demand for each skill cluster by market, explore demand for sub-skills within each cluster or view the job vacancy ratio (JVR) — defined as hiring complexity — to understand market competitiveness for these skills. The higher the JVR, the more competitive it is to recruit.
The AI filter can help you understand the ways in which skills demand has been impacted by the growth of AI.
need to know
- The data science and analytics cluster has experienced diminishing demand, as have all the skill clusters, yet it remains one of the more challenging clusters to hire for, in terms of individual skills. Predictive modeling is the second-most complex skill to hire across all clusters globally.
- Markets with particular difficulty hiring data science and analytics talent include Czechia, with a JVR of 5.5%, and France (5% JVR). Predictive modeling skills are most challenging to hire in Singapore (13.7% JVR).
- A significant share of job postings (15%) also ask for AI skills. This skill cluster, alongside cloud computing, experiences the fastest adoption among employers — and employees.
compensation.
what it shows
The data included in this graph shows the average salary brackets in U.S. dollars for data science and analytics skills in the 24 markets examined by level. Compensation data is mapped and analyzed from combined sources providing current pay data.
Select the markets of interest to understand which salary ranges are considered competitive and in which markets you should recruit to stay within budget.
need to know
- Data science and analytics is one of the best-compensated skill clusters, although there are quite significant disparities between markets.
- It also experienced an average growth in salary of 10% across most markets, although India and Mexico have seen a slight decrease.
- Talent with data science and analytics skills, along with advanced AI skills, have the highest earning potential on average among all markets and skill clusters.
remote & hybrid working.
what it shows
Remote working data shows the percentage of job postings that offer candidates remote or hybrid work for data science and analytics roles (noted as “demand”), as well as talent working preferences (noted as “supply”) in each of the 24 markets researched.
It is estimated that the actual share of remote/hybrid working opportunities is higher than advertised online. You can view the data by both skill cluster and individual skills.
need to know
- Remote and hybrid work preferences for data science and analytics talent are trending up since last year. Approximately 23-24% more talent say they prefer such opportunities compared to last year.
- Employers don’t necessarily share that view, as the amount of remote jobs available has been cut almost in half, decreasing by 45%. Meanwhile, a significant portion of those jobs have shifted to hybrid, which has seen an increase of 21%.
- Despite the gap between employees and employers on remote work, data science and analytics remains one of the top three skill clusters for remote work, and it takes the top spot for hybrid work this year.
gender diversity.
what it shows
Gender diversity data shows the current balance of male to female employees currently working in roles that require data science and analytics skills in each of the 24 markets researched. Findings are based on self-identified, normalized data from talent supply sources.
Use the chart to understand in which markets you are more likely to engage female talent with data science and analytics skills. You can view the data by both skill cluster and individual skills.
need to know
- Despite being one of the least gender diverse clusters, data science and analytics has seen only a slight drop (1%) in the female-to-male ratio year-over-year.
- Only 26% of new talent matching this skill-cluster are female (self-identified). However, those numbers are slightly higher in comparison to the most experienced talent, for which female talent comprise just 22.5% of the talent pool.
- Globally, it is approximately 3.2 times more difficult to hire female talent than male talent for individual skills within this cluster, such as predictive modeling.
take a deep dive into the in-demand skills research and find your competitive talent advantage.