The experience of being a working data scientist is not necessarily what people think. A profession that some regard as “sexy” is, more often than not, a difficult job involving long hours, tight budgets, limited staff, daunting tasks, shifting requirements, endless meetings, and inflated expectations.
For the working data scientist, pain points may dominate the fabric of their experience. High-performance data scientists are those who automate, accelerate, and streamline the more tedious aspects of their jobs so that they can focus on finding data-driven insights. They will embrace any tool, platform, or approach that can help free up mental bandwidth for tasks that demand their creativity and judgment.
Data scientists do exceptionally complex work. Their productivity depends on having access to tools and practices they can use to streamline and accelerate the details in which they immerse themselves. As discussed in this recent IDG News article , the most fulfilling experiences of high-performance data scientists fall into three broad categories:
Learning: This is the core value that data scientists deliver: learning what insights the data may reveal and what relevance they may have to the business problem at hand. According to a data scientist who was quoted in the article, “The first step is understanding the area — I’ll spend a lot of time searching the literature, reading, and trying to understand the problem.” It also involves continual reassessment of the available and appropriate data-science computational approaches, algorithms, tools, platforms, and services necessary to tackle these problems effectively within constraints of time, budget, and staff.
Collaborating: This is the process under which data scientists engage with team members, colleagues, customers, and stakeholders. These activities—such as meetings and emails–often consume a substantial part of the data scientist’s day. It involves everything from identifying a client’s business problem to assessing available data, tracking progress, discussing reports, sharing findings, explaining results, and putting the insights into an actionable business context.
Productivity in this respect depends on the data scientists’ consultative skills: their ability to guide stakeholders through the process of identifying how data-driven insights can drive disruptive business outcomes. In the words of another data scientist quoted in the cited article, “A lot of people know they need help with data, but they don’t know what they can do with it. It feels like being a magician, opening their minds to the possibilities.
That kind of exploration and geeking out is now my favorite part.”
Creating: These are the nitty-gritty data science tasks such as discovering and preparing data, building and refining statistical models, visualizing and assessing findings, and developing data-driven applications. Productivity in this respect depends on the data scientist’s ability to leverage high-performance data mining, predictive analytics, machine learning, artificial intelligence, and cognitive solutions to automate these tasks.