How far are the self-taught data scientists from their six-figure salary dream?

As data-driven strategies became more and more focal to businesses in the past decade, Big Tech companies' demand for data scientists has sky-rocketed and while data science professionals can land six-figure jobs at some of the largest companies in the world, things do not always look the same for self-taught data scientists.

Silicon Valley, where Big Tech giants like Apple, Google, and Meta are headquartered, is also home to many startups. Across this spectrum of companies, there's a need for people to make sense of their data—and more than 3,500 data scientists work in the San Jose, Sunnyvale, and Santa Clara metropolitan area, earning an average salary of more than $157,000.​ The San Francisco, Oakland, and Hayward metro area is home to more than 5,200 employed data scientists who make an average annual salary of about $153,180.

In 2012, Airbnb was one such data science-hungry startup in Silicon Valley, and Chetan Sharma was among the first people to help meet this demand. He was one of the first data scientists hired at Airbnb—the fourth member of the team, to be exact. Sharma since left to work at other startups and found his company Eppo, a product experimentation platform, where he is also the CEO. By the time Sharma left Airbnb in 2017, the data team had grown tremendously—he estimates there were about 100 employees at the end of his stint. Before landing the job at Airbnb, he earned a bachelor's degree in electrical engineering and a master's degree in statistics from Stanford University. Sharma worked in health care policy before beginning his role at Airbnb, marking the beginning of his career in data science.

Many new data scientists were attracted to the area because it allowed them to address difficult problems with cutting-edge machine learning techniques that had a substantial impact on business. This was a chance to feel like the work we were doing was more important than anything else we'd done before. However, this is not always the case. The most prevalent reason for data scientists to leave is that their expectations do not always match reality. There are various causes for this, and compiling an exhaustive list is impossible.

Because each company is different, many recruit data scientists without the essential infrastructure in place to gain the benefits of AI. This exacerbates AI's cold start problem. When you add in the fact that these companies neglect to hire senior/experienced data practitioners before juniors, you have the makings of a bad and unhappy relationship for both parties. The data scientist was probably hired to construct complex machine learning algorithms to provide insight, but they are unable to do so because their primary duty is to organize the data infrastructure and/or make analytic reports. In contrast, the firm just required a chart to present at their daily board meeting. The corporation is frustrated since they haven't seen anything.

The fact that expectations do not always match reality is the most common cause for data scientists to depart. There are numerous reasons for this, and they can't reasonably list them all, but this post is simply a summary of some of the ones that came across. In contrast, the firm just required a chart to present at their daily board meeting. Because value is not being produced quickly enough, the firm grows annoyed, and the data scientist feels unsatisfied with their work.

This emphasizes the two-way connection between the data scientist and the employer. It'll only be a matter of time before the data scientist finds something else if the firm isn't in the proper position or has goals that coincide with the data scientists. Samson Hu has a terrific piece on how Wish's analytics team was established that is enlightening for anyone interested. Another reason data scientists are disillusioned is similar to why they were disillusioned in academia: they expected them to have a significant influence on people worldwide, not just within the corporation.