A Decision Scientist stands out through unique and defining characteristics that blend technical prowess with other very important skills and intangibles.

Here are the essential attributes:¹

1. Strong Business Acumen: a keen understanding of business dynamics is critical for applying analytical skills effectively.
2. Robust Analytical Mind: this involves a solid ability to dissect problems and devise strategic solutions based on data.
3. Extensive Data Science Proficiency: mastery of data science tools and methodologies is vital for actually generating insight from data.

Similar to a Data Scientist, a Decision Scientist must possess high technical proficiency in coding languages like Python, R, and SQL. Likewise, they should also be highly skilled in advanced analytics, statistical modelling, and predictive analytics, etc. However, what really sets a Decision Scientist apart is the indispensable requirements of a strong business acumen and robust analytical mindset. They are also necessarily problem-solvers and innovators. Therefore, they must also be creative, forward-looking and think outside of the box. These aren’t contradictions, but rather attributes that describe a person capable of generating new ideas and insights, while also thinking through their implications.² These skills are not just add-ons but foundational elements that enable them to support decision-making directly and effectively.

Extensive data science proficiency is intrinsic to the role, of course.  It must empower Decision Scientists to channel their business acumen and analytical minds in new and exciting ways vis-à-vis data, generating deep exploratory analysis and statistical models that help decision-makers more easily choose between options. Ever increasingly, the intelligence harvested from data is the language of business. With these advanced skills, the Decision Scientist is capable of driving highly specific outcomes and recommendations to support decisions for which they, too, understand the underlying implications, benefits, and risks to the business. There is no longer the need for (as many) wasteful back-and-forth cycles of calibration between the “business” and “technical” sides of the team, or company.  Rather, through the Decision Scientist, these once separate “world views” become embodied in a single, unifying role — and set of capabilities — that can navigate easily across both geographies… and the business is better off for it, too!

In contrast, the Data Scientist excels in their role primarily through extensive data science proficiency. Achieving expertise in this highly technical area demands, amongst other things, endless commitment to continuous learning in a rapidly evolving field. While both roles are challenging, and share a common set of advanced analytics skills, at the core they each require different competencies and focus to be successful.

1. Data Science vs. Decision Science: What’s the Difference? ↩︎
2. Why the Combination of Analytical Skills and Creativity Is So Valuable ↩︎