There is a lot that goes around in the works of a Data Scientist. We all have now heard of Data Scientist "the sexiest job of the century" or some version of it. But what is the job that doesn't pay you well, why would it be chosen the sexiest job of the century if the payscales are low. Everyone needs money even if it is something they love to do.
So what is this Data Scientist? Over the courses of year there has been many definition of Data Scientists, here is a definition just to provide you clarity to you before digging deep in the topic. data scientist is a specific type of predictive analytics professional who applies sophisticated quantitative and computer science skills to both structure and analyze massive amounts of unstructured data (such as steaming data or sensor data), including creating and managing machine learning algorithms with the intent to derive insights and prescribe action.
The depth and breadth of data scientists' coding skills distinguishes them from other predictive analytics professionals and allows them to exploit data regardless of its source, size, or format. Through the use of one or more general-purpose coding languages and data infrastructures, data scientists can tackle problems that are made very difficult by the size and disorganization of the data.
What inspired people?
The "Big Data" craze has inspired many people to jump into corporate data science roles, including students, professors and researchers, and career changers from other business fields. This has had a number of wide-ranging effects on the talent pool itself, including opening the door for more education options like bootcamps and MOOCs (Massive Open Online Courses), tipping the typical education background towards Master's instead of PhD's among junior professionals, and blurring the line between traditional predictive analytics fields and data science.
This article is about Salaries of the Data Scientists, what are the changes in the compensation? Current compensation and what the future holds for Data Science and demographic profiles.
SALARY CHANGES OF THE DATA SCIENTIST
This year's data show that data science salaries continue to be holding steady. At all job levels, median base salaries changed by a single-digit percentage point or not at all.
Across levels 2 and 3 for individual contributors, median salaries continue to grow though the change is in the single digits.
Median salaries for level 1 individual contributors show no change. This is in contrast to 2015 and 2016 where growth was substantial for professionals at that level. This is the result of the continued influx of early career professionals with an interest in jumping into data science. We expect this trend to continue.
Salaries of data science managers are flattening or holding steady across levels this year.
As job level increases, the median base salaries of data scientists increase for individual contributors and managers. Individual contributors at level 1 earn a median base salary of $95,000, increasing to $165,000 for those at level 3. For data science managers, those at level 1 earn a median base salary of $145,000, while those at level 3 earn $250,000.
DIFFERENT SALARY DEMOGRAPHICS AND COMPENSATION
Education wise salary
- 91% of data scientists have an advanced degree: 43% hold a Master's degree, and 48% hold a PhD.
- 25% of data scientists hold a degree in statistics or mathematics, while 20% have a computer science degree, an additional 20% hold a degree in the natural sciences, and 18% hold an engineering degree.
- In every job category, data scientists who have a PhD earn median base salaries higher than those with only a Master's degree.
Industry wise salary
- By far, technology companies continue to be the largest employers of data scientists. This year, 44% of data scientists are employed in the tech industry.
- Financial services organizations employ the second largest number of data scientists at 14%.
- Across most job categories, data scientists employed by technology companies earn higher median base salaries than those employed in other industries. However, the gap between salaries in technology and other industries is beginning to close across levels among both individual contributors and managers.
Gender wise salary
- The large majority of data scientists (85%) are male.
- The highest proportion of women is seen among level 1 individual contributors, accounting for 22% of data scientists.
- As data science professionals advance in their careers, the percentage of women decreases significantly. Among the most advanced individual contributors at level 3, 6% of data scientists are female; 10% of executive (level 3) managers are female.
- Across all levels, this is below the proportion of women seen in predictive analytics.
Experience wise salary
- The median years of experience among data scientists is six, indicating that the field continues to attract many young professionals.
- 45% of data scientists have 5 or fewer years of experience, and 72% have 10 or fewer years. The field as a whole continues to trend young as more professionals enter the data science field.
PREDICTION FOR THE FUTURE OF DATA SCIENCE
As the data science field continues to accelerate, proliferate, and evolve with changes in technology, we have several predictions on what trends will be integral to its development and expansion over the next few years.
The Push for ROI will Hound Both Legacy Organizations and Startups
Substantial investment has been poured into data science, both as traditional organizations start to collect massive amounts of unstructured data and as startups seek to use data in new ways to disrupt industries or introduce new products. In both cases, pressure has been high for data science teams to deliver returns on their significant promises.
Legacy organizations that are newly-adding data science technologies to their processes will want to see swift justifications for the investment, whereas startups that contain data science as an integral part of their DNA need to be able to show investors that they're viable in the long-term-not just as an intriguing sales pitch.
Specialists Will Become the Norm, Not "Unicorns"
In years past, it was usually expected that a data scientist be able to oversee every aspect of the analytical lifecycle, from data wrangling to analysis to visualization and more. Now that many teams have grown, the need for generalist "unicorns" has given way to a higher demand for data science specialists that can work with other analytics professionals and data engineers on the team. For example, we now regularly receive requests for experts in specific areas such as NLP (natural language processing) or image processing using tools like TensorFlow.
That being said, generalists may still be in demand for firms hiring their first data scientist, or for those that have many, varied use cases for data science and therefore require a wide range of skills. Hiring "unicorns" with broad skillsets used to be the norm in data science, but now specialists, as well as generalists, are in high demand.
For the most part, as many firms have further developed their exact needs, hiring managers are searching for more specialized skills in areas of data science. Additionally, as the supply of data scientists and their education options increase, we're beginning to see professionals segmenting into specialized areas of data science because they have more opportunities to do so.
The Hands-On Component Will Be Essential to Leadership
For the longest time companies have advocated continuous learning to be at the centre of a long-term career strategy, but, now more than ever, staying hands-on has become a key component, even for senior leadership positions.
In a field that evolves so quickly and where leaders are often expected to exhibit a "player coach" mentality, remaining hands-on with the data is the best way to stay current with new tools and technologies. In cases where a role doesn't necessarily accommodate this strategy, some senior-level data science managers choose to spend a portion of their personal time taking part in quantitative competitions to stay sharp. Data scientists who find themselves straying too far away from technical work may quickly find their skills out of date and unmarketable in today's climate.
In closing, for the past several years we've pointed to several growing trends that show how data science has begun to spread beyond its original borders. Predictive analytics professionals that have typically worked with structured data have begun to transition their skills into data science roles, data science is no longer limited to giant tech firms and startups on the coasts, and many industries outside the typical "digital native" profile are adding data science technologies to their repertoire.
These changes have not only had a flattening effect on some of the salary disparities we've seen in years past, but it has also led to increased opportunities, both for data scientists looking to live somewhere besides Silicon Valley and for firms in the Middle U.S. who have struggled to lure data scientists out of their coastal geographies. We expect to see more industry shifts as the use of data science continues to spread and mature, and look forward to sharing them in future reports.