Data scientist jobs are hot at the moment. CompTIA’s recent “State of the Tech Workforce” report predicted that job openings for data scientists (along with data analysts) will grow by 5.5 percent over the next 12 months. Surely that level of demand translates into superior compensation, right?
That assumption is correct: Dice’s most recent Tech Salary Report pins the average data scientist salary at $117,241, having decreased 2.8 percent between 2021 and 2022. That decrease isn’t a negative; more companies embracing data science encourages more people to join the profession to take advantage of new opportunities, helping drive down demand (and lowering compensation a bit).
At some companies, data scientists can easily make six figures in salary, bonus, and stock options. Levels.fyi, which crowdsources compensation data from a range of tech companies, has a breakdown of the top-paying companies for data scientists:
That Netflix tops this list should come as no surprise; the company has a solid reputation for paying its tech professionals a considerable amount of money, with the expectation those employees will deliver superior performance. The other companies on this list, from Airbnb to Instacart to Lyft, generally have the biggest of Big Data challenges, which in turn require data scientists with exemplary skills. To put it another way: If a data scientist tasked with making nationwide logistics more efficient isn’t making six figures per year, something is very wrong.
If you want to break into data science—and unlock a potentially lucrative salary—you need to learn a core set of essential skills. According to Lightcast, which collects and analyzes millions of job postings from across the country, some of the core technical skills for data scientists include:
- Statistics (i.e., statistical analysis)
- Data processing
- Data visualization
- Data storage
- Programming languages (Python, R, and more)
- Machine learning
- Artificial intelligence
Master data scientists can also use their intuition to surface crucial insights from messy or incomplete datasets, but that skill can often take years to fully develop. If you’re interested in exploring data science as a profession, start by sampling these free resources:
- Google—Machine Learning Crash Course
- CalTech: Learning from Data
- Codementor Data Science Tutorials and Insights
- KDNuggets Tutorials
- R-bloggers Tutorial: Data Science with SQL Server R Services
- Open Source Data Science Masters
- Simply Statistics
Fortunately, there are multiple pathways to becoming a data scientist. Explore your options to see what works best for you—and if you master the necessary skills, you can launch a potentially lucrative career.