What does a data scientist do?

One of the nice things about data science is that it touches on a lot of different fields. If you're a people person, you might gravitate towards more of a business analyst role, or if you're more interested in engineering and the math involved, then you might get into more of a machine learning engineering role. There's a lot of middle ground there too.

It's important that you're curious, persistent, and precise. Good communication skills are a major plus, and an interest and solid understanding of the math, as well as some programming skill, is essential. The amount and mix of these depends on the exact role.

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Most individuals who enter the field earn a master’s degree, suggesting that five to seven years is the length of the average educational track for data scientists.
• Bachelor’s Degree – four years
• Master’s Degree – one to three years

Almost half of all data scientists have a Ph.D.

The timeline for those who earn a bachelor’s degree and pursue a doctorate without first completing a master’s degree is between eight and ten years:
• Bachelor’s Degree – four years
• Doctoral Degree – four to six years

The timeline for those who pursue a master’s degree en route to their doctorate is between nine and thirteen years:
• Bachelor’s Degree – four years
• Master’s Degree – one to three years
• Doctoral Degree – four to six years

For most data scientists, the path to entering the profession entails earning a master’s degree, pursuing professional certifications, and welcoming career-long learning to remain current in what is an always-evolving field.

It goes without saying that data scientists need a background in math and statistics and a familiarity with several programming languages. This technical knowledge, however, is not the stand-alone requirement to work in the field. The best data scientists also bring some particular personality traits to work:

A curious nature
Because there are so many areas and so many data points to analyze in the field, data scientists must have an inherent curiosity that drives them to explore new territories to solve problems and find answers.

Organizational skills
The only way for data scientists to reach the right conclusions is to keep track of millions of data points and make sure information is organized in a useful way.

Communication skills
Data scientists understand data better than anyone else. However, to be successful in their roles, and for their organizations to benefit from their services, they must be able to convey the correct message and their insights to both technical and non-technical audiences.

Business acumen
Business know-how and an understanding of the elements that make up a successful business model are critical for data scientists. Without these, their technical skills cannot be channeled productively to discern and solve problems which are standing in the way of sustaining and growing the business.

Focus and persistence
Data scientists encounter their fair share of frustration, especially when it seems like there is no answer to the problem at hand. The capacity to remain focused and keep reorganizing, reanalyzing, and reworking the data is the only route to a ‘Eureka’ moment.

Data intuition
This is without doubt one of the most significant non-technical skills – one that comes with experience – that a data scientist needs. Data intuition is the ability to perceive patterns where none are observable on the surface; to discern where in the unexplored pile of data bits the value lies. This skill is developed by questioning if the data makes sense, by asking questions like, Are the features meaningful? Do they reflect what you think they should mean? Given the way your data is distributed, which model should you be using? What does it mean if a value is missing, and what should you do with it?

Adeptness at working with unstructured data
Data scientists are familiar with highly organized or structured data. But, they must also learn how to work with unstructured data – that is, collections of information stored outside a database, such as large agglomerations of event or security logs, e-mail messages, customer feedback responses., and other text repositories. For example, a data scientist working with a marketing team to identify insights into consumer behavior, will be much better equipped for the project with an understanding of social media and the kinds of information or data these media can provide.

The answers to these questions provide some further insight into a career in the field of data science:

Why is there an increased demand for data scientists?
In today’s world, nearly every company has the ability to collect data, and the amount of data is growing larger and larger. This has led to a higher demand for people with specific skills, who can effectively organize and analyze this data to glean business insights.

What are some pros and cons of working in the data science field?
Pros
• Significantly above-average pay scale
• Variety – opportunities to gain a wide perspective by working for a wide variety of companies and coming up with solutions and information related to customer retention, marketing, new products, and general business solutions
Cons
• Extreme variety – sometimes leading to frustration from not being able to dive fully into a specific topic
• Constantly evolving systems and software – sometimes resulting in confusion in determining which are the best for a specific project

Based on our pool of users, data scientists tend to be predominately investigative people. They access information from large databases, use code to manipulate data, visualize numbers in a digital format, and convert data to actionable insights about everything from product development to customer retention to new business opportunities. It is hard to imagine a profession that is more investigative.