Data science has become one of the hottest and most in-demand careers in recent years. As companies increasingly rely on data to drive business decisions, the need for skilled data scientists has skyrocketed. But is the demand for data scientists really as high as many claim? Let’s take a detailed look at the evidence.
The exponential growth of data
The rise of data science as a career is tied directly to the enormous growth in data that has occurred over the last decade. As one measure, the research firm IDC predicts that the total amount of global data will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025. That’s a staggering 430% increase in just 7 years!
This explosion of data is coming from many sources: social media platforms, ecommerce websites, smartphones, IoT devices, and more. All this data holds valuable insights for businesses – but only if they can extract meaning from it. And that’s exactly what data scientists are trained to do. As long as data continues to grow at an exponential rate, demand for data science skills will likely follow suit.
Data scientist job openings
One of the most direct measures of demand for data scientists is the number of job openings. Data scientist roles have seen significant growth in recent years. According to job search site Indeed, data scientist job postings have increased by over 650% since 2013.
The following table shows the rapid increase in data scientist job postings on Indeed in recent years:
| Year | Data Scientist Job Postings |
|---|---|
| 2013 | 1,736 |
| 2014 | 2,906 |
| 2015 | 6,356 |
| 2016 | 12,065 |
| 2017 | 17,424 |
| 2018 | 26,975 |
| 2019 | 31,809 |
As this data shows, even with minor fluctuations year-over-year, the overall trend is clearly upwards. In 2019 alone, there were over 30,000 data scientist job openings posted on Indeed.
High compensation
Another indicator of strong demand is compensation. As demand rises for a skillset that is also scarce, salaries tend to rise as companies compete for talent. Data science is no exception to this economic principle.
According to Glassdoor, the average data scientist earns $117,288 per year in the United States as of October 2019. This is significantly above the median household income of $63,179.
Data science salaries also tend to be far above average compared to other common technology roles:
| Job Title | Average Base Salary |
|---|---|
| Data Scientist | $117,288 |
| Software Engineer | $104,574 |
| Computer Programmer | $87,608 |
| Network Architect | $113,529 |
High pay reflects the high demand and low supply of candidates with data science skills. As long as demand continues to outpace supply, salaries can be expected to remain elevated.
Talent shortages
Speaking of supply, there is consistent reporting of talent shortages by companies seeking to hire data scientists. As a relatively new field, there simply aren’t enough job candidates currently who possess the required education and experience.
A 2019 report by LinkedIn identified data scientists are among the top emerging jobs of 2019. The supply of candidates for these roles cannot keep up with demand, leading to major talent gaps.
Some key stats on the data science talent shortage:
– 63% of data scientists come from only 8 tech hubs in the US like Silicon Valley and NYC. This lack of geographic diversity makes hiring challenging.
– It takes an average of 45 days for companies to fill a data science role – much longer than for most other roles.
– 56% of data science leaders say their teams are understaffed and they struggle to fill open job requisitions.
The lack of data science talent is felt across many industries. As examples, there is strong demand for data scientists in healthcare, retail, finance, and technology. Nearly every modern company now relies on data analytics to operate efficiently.
Until more data science training programs produce graduates, demand will likely continue outpacing supply.
Rising demand from startups
Large technology companies like Google, Facebook, and Amazon have long hired data scientists. But the demand is also skyrocketing among new startups.
CB Insights analyzed the fastest growing job roles at tech startups. They found that demand for data scientists grew by 344% from 2015 to 2019. This reflects how crucial data analytics has become, even for young companies.
Startups need data scientists from day one to:
– Optimize marketing and user acquisition
– Develop data-driven product features
– Personalize experiences for each customer
– Forecast future business performance
For most modern startups, data science is not a nice-to-have but a must-have. Investors will also look for data science hires to determine if a startup is analytics-driven.
The surge in data-focused startups like Databricks and Civis Analytics also creates new data science jobs. As long as startups continue thriving, demand will rise.
Transition to cloud computing
The transition from on-premise data storage to cloud-based storage is also accelerating demand for data skills. As organizations migrate their data platforms to the cloud, they require data scientists who understand tools like Amazon AWS, Microsoft Azure, and Google Cloud Platform.
This chart from Indeed shows the rising demand for cloud skills like Google Cloud Platform (GCP) among data science job postings:
| Skill | Share of Data Scientist Job Postings Requiring Skill |
|---|---|
| R | 49% |
| Python | 47% |
| Hadoop | 26% |
| Amazon AWS | 22% |
| SQL | 21% |
| Google Cloud Platform | 20% |
| Spark | 19% |
Cloud platforms provide the scale needed for large-scale data science and machine learning initiatives. Data scientists with cloud expertise are in hot demand.
Rising value of data
The fundamental driver behind rising data scientist demand is the increasing value of data to businesses. According to research firm IDC:
– Worldwide revenues for big data and business analytics will grow from $189 billion in 2019 to $274 billion by 2022.
– 80% of businesses are expected to integrate analytics into their operations by 2022.
As data analytics becomes more crucial for success, companies will need more data scientists to extract insights. With data’s value rising every year, demand for data science skills shows no signs of slowing.
Machine learning adoption
Another key driver of demand is machine learning. Machine learning models require immense amounts of data to train properly. As more companies adopt machine learning, their need for data scientists grows.
IDC forecasts worldwide revenues for machine learning applications growing from $12 billion in 2019 to over $57 billion by 2022. That’s a compound annual growth rate of 44% in just 3 years!
Leading machine learning applications like product recommendations, chatbots, logistics optimization, predictive maintenance, and financial risk modeling all demand data science expertise to develop.
As executives become more aware of machine learning’s benefits, their demand for data scientists with ML expertise will rapidly rise.
Automation of simple analytics
Some believe that new automated tools like AutoML could reduce the need for data scientists. But in reality, these tools automate just the simple parts of analytics, like model building. Data cleaning and preparation still requires human judgment, creativity, and domain expertise.
As analytics software automates the simple stuff, data scientists have more capacity to focus on high-value initiatives like inference, causal modeling, and business integrations.
Forrester predicts that AutoML adoption will grow from 7% of companies in 2019 to 25% by 2021. But these tools complement data scientists rather than replace them.
New analytics techniques and data types
Data science is an innovative field with new technologies constantly emerging. Data scientists must keep up with the latest techniques like natural language processing, deep learning, reinforcement learning, graph analytics, etc.
As techniques change rapidly, companies have an insatiable demand for data scientists with the latest skills. Staying still means falling behind the competition.
Data scientists also need to handle new, unstructured data types like images, video, audio, and text. Roles keep evolving as data types and analytics techniques expand.
Business adoption across functions
Initially, data science was siloed primarily within tech teams. But now businesses are applying analytics across functions like:
– Marketing (sentiment analysis, customer segmentation, campaign attribution)
– Finance (forecasting, algorithmic trading)
– HR (workforce planning, attrition risk modeling)
– Manufacturing (predictive maintenance, operational optimization)
Data is now used widely for strategic planning and decision making. Even frontline roles are becoming more data-driven. This broad integration of data science across organizations drives demand.
Conclusion
In conclusion, current evidence overwhelmingly suggests high and growing demand for data science skills. Given data’s increasing strategic value, companies across industries are ramping up investments in data science teams dramatically.
However, supply has not yet caught up, leading to intense competition for scarce data science talent. Demand is likely to keep outpacing supply for the foreseeable future. While tools will automate some basic analytics tasks, they cannot replace the creativity and critical thinking of skilled data scientists.
For job seekers interested in a promising career path, and for employers needing analytic expertise, data science is clearly one of the hottest areas with sustainable demand for the long-term. The “sexiness” of data science may fade over time, but its strategic business value will only grow.
