If you are looking for a career which can be applied to multiple industries and can lead to a vast array of interesting and unique projects, look no further than data science.
Dubbed by Harvard Business Review as "the sexiest job of the 21st century", data science is a field that drives innovation, feeds your creative spark, and has the ability to illuminate the world around us. These characteristics, plus the above-average compensation the job provides, are likely the main contributing factors that make data science rank highly on the lists of desirable jobs each year.
Jobs Data Scientists Actually Do
There is much debate about how to accurately define the job of a data scientist, largely because the requirements for data scientists vary greatly depending upon their chosen industry focus. HBR provides a good overview of the job, however, observing:
"More than anything, what data scientists do is make discoveries while swimming in data. It's their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data."
Some jobs a data scientist might be asked to perform include:
- Framing open-ended questions and conducting research to answer those questions.
- Extracting huge amounts of data from both internal and external sources.
- Preparing data for predictive and prescriptive modeling via analytics programs, machine learning, and statistical methodologies.
- Exploring and examining data to determine trends and patterns that can lead to actionable insights.
- Inventing new algorithms to solve problems and new tools to automate work.
Data scientists, then, are more than simply numbers crunchers. Forbes explains:
"Data scientists utilize their knowledge of statistics and modeling to convert data into actionable insights about everything from product development to customer retention to new business opportunities. They understand statistics and applied mathematics. They can test hypotheses with experiments they design. They know enough programming to engineer methods for sourcing, processing, and storing their data. And they communicate their findings through data visualizations and stories."
Different Types of Data Scientists
The designation "data scientist" is actually an umbrella term under which exist a variety of different, specialized descriptive types. SaaS guru Tom Tunguz divides data scientists into a few recognizable categories. Some of them are:
Quantitative, exploratory data scientists: These data scientists combine theory and exploratory research to improve products. Typically, data scientists of this type have PhDs and may have strong backgrounds in physics or machine learning.
Operational data scientists: Working in fields like finance, sales, or operations, these data scientists have a strong background in analytics and statistics. They may concentrate in areas such as business intelligence, defining patterns and trends and using predictive analytics to produce actionable insights.
Product data scientists: These professionals focus on understanding the ways users interact with a product and finding ways to improve or enhance the product accordingly. They work closely with or act as product managers and engineers.
The field of data science, then, covers a huge amount of ground, running the gamut from analysts who use business intelligence tools to physicists writing code for innovative technologies such as self-driving cars and the like.
In this video, a Facebook data scientist describes his job:
Common Personality Traits among Data Scientists
Albert Einstein said two things that epitomize the personality traits needed to become a successful data scientist. First, he said: "It's not that I'm so smart. It's just that I stay with problems longer." In a similar vein, he observed: "I have no special talent. I am only passionately curious."
Successful data scientists are master problem solvers. Their curiosity to know, to explore, and to get to the bottom of an issue are character traits that define the best data scientists, no matter the industry in which they work. Sean McClure, Director, Data Science at Space-Time Insight, makes this observation:
"Instead of listing languages and tools in an attempt to engineer your future go solve a problem. Go solve a hundred problems. Then take a look at the list of skills you have; the languages you know, the technologies you've mastered, and the approaches you take. Your career will always be a byproduct of the challenges you've tried to solve."
Data scientists also need to be good communicators. They must be able to take highly complex information and communicate it in a way that is easy both for technically-savvy and technically-challenged audiences.
Common Skills and Educational Requirements for Data Scientists
Skills you may need for becoming a data scientist include:
- Math skills such as linear algebra, calculus, probability, and statistics
- Machine learning tools and techniques
- Software engineering skills
- Database management skills
- Languages and applications such as Python, R, SQL, Java, C, C++, SPSS, Tableau, and Hadoop
Paysa examined job postings for data scientists across multiple industries. Below is a quick chart of the top skill prerequisites found among those job listings:
There are 3 common educational paths for data scientists:
Degrees and graduate certificates provide structure, internships, networking and recognized academic qualifications for your résumé. Majors that dovetail nicely into common data science careers include: statistics, mathematics, economics, operations research, and computer science.
MOOCs and self-guided learning courses allow you to complete projects on your own time, but they require you to structure your own academic path. Choosing this method of learning requires you to do your own networking when it is time to find a job.
Bootcamps may be taught by practicing data scientists and may be a quick way to acquire some of the skills you need. The bootcamp model is based on experiential learning, and it does present some opportunities to network to help you with job placement.
Generally, to get the kind of position you want as a data scientist, having a degree is the preferred course. Paysa compiled hiring information for top data scientist positions. Here are the results of Paysa's research regarding educational requirements:
Professional Associations and Organizations for Data Scientists
Professional organizations for data scientists include:
- Data Science Association
- International Institute for Analytics (IIA)
- International Machine Learning Society (IMLS)
- Institute for Operations Research and the Management Sciences (INFORMS)
Rock Stars of the Data Science World
Here are a couple of well-known names in data science today:
Hilary Mason: Founder of Fast Forward Labs, Data Scientist in Residence at Accel Partners, and former Chief Scientist at bitly, Mason is known for turning "big data into plain English". Check out this short video of Mason in action.
Peter Norvig: Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google's core search algorithms group, and of NASA Ames's Computational Sciences Division, making him NASA's senior computer scientist. Check out this TED Talk with Norvig as speaker.
Data Scientist Salaries
Just as the job description of data scientists changes according to industry, so too the compensation for data scientist jobs changes. Based on 8,000 profiles gathered by Paysa, the average base salary for data scientists is $106,000 per year, ranging from $80,000 to $195,000. The average market salary for data scientists is $167K per year, which includes $106K base salary, $20K annual bonus, $12K signing bonus and $41K annual equity.
Average salary for data scientists, per Paysa data.
Of course, salaries for data scientists are dependent upon both the companies for whom they work and the location of their employment. For instance, let's take a look at how salaries differ according to company. The top four companies for data scientists are: Facebook, Microsoft, Twitter, and Apple.
Facebook: The average base salary for Facebook data scientists is $149,713 per year, ranging from $124,410 to $176,100. The average market salary is $305K per year, which includes $150K base salary, $21.6K annual bonus, $30.3K signing bonus and $104K annual equity. 71 percent need to know Python. 93 percent need to have a bachelor's degree. Average time to promotion for Facebook data scientists is 2.9 years.
Facebook data scientist salaries, per Paysa data.
Microsoft: The average base salary for Microsoft data scientists is $159,088 per year, ranging from $131,267 to $188,143. The average market salary is $268K per year, which includes $159K base salary, $39.3K annual bonus, $22.9K signing bonus and $47.4K annual equity. 51 percent need to know Data Mining. 96 percent need to have a bachelor's degree. Average time to promotion for Microsoft data scientists is 2.9 years.
Microsoft data scientist salaries, per Paysa data.
Twitter: The average base salary for Twitter data scientists is $143,182 per year, ranging from $121,055 to $166,173. The average market salary is $276K per year, which includes $143K base salary, $26.4K signing bonus and $106K annual equity. 65 percent need to know Python, and 90 percent need to have a bachelor's degree. Average time to promotion for Twitter data scientists is 23 months.
Twitter data scientist salaries, per Paysa data.
Apple: The average base salary for Apple data scientists is $144,114 per year, ranging from $118,708 to $170,655. The average market salary is $217K per year, which includes $144K base salary, $24.5K annual bonus, $26.8K signing bonus and $43.1K annual equity. 82 percent need to know Python, and 91 percent need to have a bachelor's degree. Average time to promotion for Apple data scientists is 3 years.
Apple data scientist salaries, per Paysa data.
Top locations for data scientists, along with base and market salaries, are:
Seattle, WA: Base Salary $150K; Market Salary $243K
San Francisco, CA: Base Salary $125K; Market Salary $207K
San Jose, CA: Base Salary $128K: Market Salary $201K
Boston, MA: Base Salary $106K; Market Salary $157K
New York, NY: Base Salary $98K; Market Salary $140K
How to Make Wise Career Decisions with Data
As all these stats indicate, it pays to do some research when considering a data scientist job. Paysa is a great resource because it can be personalized to give you specific skills and job recommendations, as well as salary data to help you negotiate a job offer or promotion with confidence. You can check out information on current data scientist positions here.
One happy data scientist and Paysa user from Atlanta, GA reports: "Paysa helped me understand my market value by supplying real data. There are hundreds of conflicting posts about the salaries of data scientists and this platform helped me out through the noise. It helped me realize that the first salary I took in the field was about 10 percent lower than market, and it helped me negotiate a 13.5 percent raise after my first year!"
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