Data science has been one of the hottest career paths of the last decade. But with economic uncertainty looming in 2023, some are questioning whether demand for data scientists will continue. In this article, we’ll examine the job outlook for data scientists and whether these roles are still likely to be in high demand.
What is a data scientist?
Data scientists are analytical experts who utilize data to solve complex business problems. Their day-to-day work involves collecting, cleaning and organizing data, performing statistical analysis and machine learning, and creating models to predict future outcomes and behavior.
Data scientists have skills spanning computer science, mathematics and statistics. Many have advanced degrees in fields like machine learning, computational statistics, or other quantitative disciplines. The bottom line is that data scientists extract insights from data.
Why has demand for data scientists exploded?
In general, demand for data scientists took off for a few key reasons:
- Data proliferation – As digital transformation accelerated over the last 10-15 years, the amount of data companies generate and capture has exploded. By one estimate, the world creates 2.5 quintillion bytes of data daily. Organizations need data scientists to make sense of all this information.
- Competitive advantage – In a data-driven world, businesses rely on data to optimize processes, uncover market trends, retain customers, and gain a competitive edge. Data scientists are key to leveraging data for competitive advantage.
- Advanced analytics – Modern data science leverages sophisticated machine learning algorithms to uncover hidden patterns and make predictions – tasks that require highly skilled data scientists.
- Big data technology – New big data tools like Hadoop and Spark enabled companies to store and analyze vast amounts of data cost-effectively. However, data scientists are still needed to operate these tools.
In short, the modern data explosion, the rise of AI/ML and the digital economy’s insatiable demand for data drove massive demand for the scarce talent of formally trained data scientists.
What do data science job postings reveal about demand?
Data science job postings provide a window into employer demand. Career site Glassdoor reported a 34% annual increase in data scientist job openings in 2020. LinkedIn’s 2020 Emerging Jobs Report also found data science roles growing faster than any other job category.
Here are some key stats on data scientist job postings:
- In 2020, there were over 97,000 data scientist job listings on LinkedIn (up 46% from 2019)
- Data scientist job postings increased by over 650% from 2013 to 2020
- The supply of data scientists remains far below employer demand. For every 1 data scientist, there were roughly 6 job listings in 2020.
These figures indicate sky-high demand for data science skills prior to the pandemic. But has demand softened in light of recent economic uncertainty?
Has demand for data scientists declined in 2022/2023?
Despite rising recession fears, employer demand for data scientists has remained extremely robust through 2022 and into 2023.
LinkedIn’s 2022 Emerging Jobs report again ranked data scientist as the #1 emerging job. And recent data on data science job postings shows:
- Active job listings for data scientists grew 19% year-over-year in 2022
- In Q3 2022, there were over 100,000 open job postings for data scientists in the U.S. alone
- Data science listing made up 2.3% of all job postings in Q3 2022, but only 0.5% of all job seekers in the U.S. had data science skills
Moreover, the typical data scientist salary remains high. According to Glassdoor, the average base pay for a data scientist in the U.S. was $120,000 in 2022. The strong pay indicates employers remain willing to pay top dollar for data science talent, even amidst macroeconomic uncertainty.
In short, data suggests employer demand is still extremely strong despite some economic warning signs.
Will demand for data scientists decline in the near future?
Although data scientist job postings have continued growing, it’s fair to wonder if demand will slow in the near future. Economists increasingly warn of a potential recession in 2023/2024. Could cooling economic activity diminish demand for data scientists?
Industry experts think demand will remain resilient even if economic growthslows. Here’s why:
- Data science delivers ROI – Data science capabilities deliver demonstrated return on investment by optimizing operations, reducing costs, avoiding risks, and identifying new revenue opportunities.
- Competitive necessity – In a data-centric business climate, companies will be reluctant to scale back advanced analytics capabilities that provide competitive advantage.
- Shortage of talent – With data scientist talent still severely limited, companies won’t easily relinquish the data scientists they already employ.
- Ongoing digital transformation – Demand should remain robust as companies continue migrating business processes to the cloud.
While a severe economic contraction could impact demand at the margins, data science remains an essential function for digitally transforming organizations. Data scientists deliver too much business value to be treated as a discretionary expense.
Will automation and AI reduce need for data scientists?
Some believe that as artificial intelligence and machine learning mature, fewer data scientists will be needed. After all, aren’t machines ideally suited to perform repetitive, analytical tasks?
It’s true that AI and ML tools have grown more capable and accessible for non-experts. But these technologies complement data scientists more than replace them. Here are some reasons data scientists remain essential, even with improving AI/ML:
- Data science is multidisciplinary skillset. It requires a combination of technical skills, business acumen and soft skills that AI lacks.
- Data scientists oversee and maintain AI/ML models in production. They monitor model performance and retrain models when necessary.
- Many tasks like data cleaning and prep, feature engineering, statistical analysis and model validation still require human judgment, creativity and critical thinking.
- Data scientists provide strategic guidance and communicate data insights to business leaders who make decisions but often lack technical expertise.
In short, AI makes data scientists more productive. But it mainly handles repetitive tasks, freeing up data scientists to focus on higher-value analysis and decision support.
Which industries have the greatest need for data scientists today?
Although data science is valuable across industries, some sectors have particularly acute demand. Industries currently hiring the most data scientists include:
- Tech – Tech firms like Google, Facebook, Apple and Amazon employ hordes of data scientists to analyze product usage, optimize algorithms, forecast demand, reduce churn and more.
- Finance – Banks, insurance companies, hedge funds and other financial institutions use data science to guide investment decisions, quantify risk, detect fraud, comply with regulations and improve customer experience.
- Healthcare – Data science supports improved patient diagnoses and treatment, clinical decision-making, hospital operations and medical research.
- E-commerce and retail – Retailers and e-commerce platforms leverage data science to understand buyer behavior, personalize recommendations, forecast inventory needs, set prices and run targeted promotions.
- Manufacturing – Smart manufacturing powered by data science optimizes supply chains, increases equipment reliability, improves product quality and minimizes downtime.
Data science supports nearly every function across these industries including marketing, sales, finance, HR, R&D and customer service. As a result, demand remains massive.
Will smaller companies also need data scientists?
Larger enterprises have driven most early data science adoption. But smaller firms increasingly run data-driven operations too. For small and mid-size businesses, key data science applications include:
- Optimizing marketing campaigns and go-to-market strategies
- Improving inventory and production planning
- Segmenting customers and personalizing experiences
- Forecasting product demand
- Analyzing customer churn and retention
Scaling data science is undoubtedly harder for resource-constrained smaller companies. But cloud computing and managed services are making advanced analytics more accessible for SMBs. Low-code ML tools also allow business users to tackle some basic data science tasks. Still, small companies need formally trained data scientists to oversee complex analysis and strategic decision-making.
What data science skills are most in-demand today?
Data science sits at the intersection of multiple technical skills. Here are some of the key capabilities employers prioritize when hiring data scientists:
- Python and R – Proficiency in Python and/or R is essential for any data scientist. These programming languages are widely used for data analysis and building ML algorithms.
- SQL/NoSQL databases – Data scientists need to extract and query data from databases like SQL, MongoDB, Cassandra and Redis.
- Machine learning – Data scientists must have working knowledge of ML algorithms like regression, random forest, boosting, neural networks, etc.
- Big data tools – Experience with distributed data processing platforms like Spark, Hive and Hadoop is valued.
- Cloud platforms – Data science workloads are increasingly cloud-based, so AWS, GCP and Azure skills are useful.
- Math and statistics – Having an analytical mindset and solid grasp of concepts like statistical inference is mandatory.
Of course, data scientists need more than just technical abilities. Storytelling with data, critical thinking and business acumen are equally important.
How can current data professionals upskill into data science roles?
Given intense employer demand, current data professionals may want to upskill into data science roles. Here are tips for making the transition:
- Take online data science courses to build ML, Python, R and statistics skills – many top programs are available
- Learn via free resources like Kaggle competitions, GitHub libraries and YouTube tutorials
- Practice by analyzing open datasets from sites like data.world and Kaggle to hone abilities
- Complete a data science certificate program or nanodegree bootcamp
- Consider a master’s degree in data science if you lack formal credentials
- Highlight analytical projects, visualizations and technical skills on your resume
- Apply for junior data science roles to get a foot in the door
It takes dedication to pivot into data science from a different career background. But for motivated candidates, data science offers tremendous opportunities.
Will demand endure for data scientists in the long run?
Looking 5-10 years out, data scientists appear poised to remain in red-hot demand. Here’s why:
- The volume of data generated will continue expanding exponentially, driven by IoT sensors, digital engagement, automation, and more.
- Data is increasingly central to decision-making, meaning companies will prioritize analytics even more in the future.
- AI/ML will become even more mainstream, and data scientists will be needed to develop business applications.
- New data technologies and architectures like streaming analytics and data fabric will require data skills.
- Data privacy and governance are growing concerns, requiring data professionals to ensure compliance.
While it’s impossible to predict the future, the long-term outlook for data science appears extremely bright. Data will only grow more important over time.
Conclusion
Despite potential economic headwinds, demand for data scientists remains near an all-time high. Data science delivers tremendous business value in the digital economy, and companies are clamoring for scarce data talent. While AI and ML can automate some basic tasks, they mainly augment data scientists. For the foreseeable future, formally trained human data scientists appear irreplaceable.
Candidates interested in breaking into data science should continue developing technical abilities along with business skills. With the right blend of strengths, aspiring data scientists can take advantage of this unprecedented career opportunity.
