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Five Characteristics of a Good Data Scientist

With the popularity of data science, schools around the world are training students in the necessary technical capabilities. In addition to these skills, future data scientists need certain personality traits to be successful.

• By Troy Hiltbrand
• June 13, 2016

As we have all heard, Glassdoor classified the data scientist as the best job of the year. Programs of study are popping up in some of the most prestigious universities across the world trying to fill the demand in the market. These programs focus on the technical competencies associated with the job, but those skills are only a portion of the formula for success.

Some inherent characteristics separate great data scientists from lackluster data scientists.

#1: Business Understanding -- The value of the data scientist to a business is not that they can apply statistical modeling to data to generate a model. A data scientist needs to understand the business's needs and develop analytics that meet those objectives.

Analytics results can include enhanced customer engagement, automation resulting in cost optimization, or business process optimization saving time and labor. However, real value comes from delivering the results that match the actual business need.

For Further Reading: Data Scientist Skills for Success Data Science Is More than Data Data Science for Everyone

#2: Passion -- Data science is as much an art as it is a science. The data scientist should have in mind a general idea of what a great solution looks like. Mediocre solutions are plentiful. Finding the right solution for the right situation takes patience and determination.

A data scientist has to keep pushing to the solution that will optimize business value. Without passion for the business and passion for the field of study, a data scientist will stop short of finding that optimal solution.

#3: Curiosity -- Data science is not a new field, but new discoveries are made every year. This is because great data scientists are always looking for alternative ways to solve problems. This includes searching for new and optimal ways to acquire and merge data, preprocess and engineer features, or develop models and improve their run time using a combination of software and hardware optimizations.

#4: Innovation -- Some of the value in data science is coming up with solutions that have not been thought of and executed before. In digital business, first-mover advantage is real and can make or break a business. Many new business models hinge on how well they can leverage data and analytics to produce a new and innovative model, so data scientists cannot just replicate what has worked before. They must always be looking for the next big thing that will distinguish their offering from others already in the market.

#5: Intuition -- Although the math involved in analytics is foundational and proven, using it to solve specific business problems is an art form, as mentioned above. The data scientist must be able to differentiate great from not-so-great analytics.

Accuracy measures are an excellent mechanism for testing the production readiness of a model, but the data scientist needs to be able to feel when a model is ready. They also need that intuition to know at what point the production models are stale and need to be refactored to respond to an evolving business environment.

Although technical skills are paramount for a data scientist's success, many important characteristics are inherent and cannot be taught in a class. These characteristics can be acquired, but it takes time and practice and requires internal desire.

The combination of learned skills and intrinsic characteristics is what differentiates a great data scientist from a mediocre one.