Data scientists are highly sought after professionals across tens of thousands of companies. And there’s a difference between what a data scientist does and what a statistician does.
The field of statistics has been around for centuries, while data science programs at universities are now established and enrolling many interested students.
In statistics, hypotheses are evaluated. In data science, hypotheses are generated. Statistics is often considered a primary data analysis technique, while data science is often considered secondary. Data scientists generate hypotheses while statisticians evaluate hypotheses.
Data scientists aren’t just found in tech companies. Titles that include the word “data science” are common in job descriptions across fields in the natural resources.
Despite the relative infancy of the field, there are different “flavors” of data scientists. The different skills that data scientists have can bring added value to any forestry company. In 2018, Elena Grewal outlined three kinds of data scientists in a LinkedIn article. It is still useful today to understand the different roles and responsibilities of data scientists.
Elena was the Head of Data Science at Airbnb, a company that receives over 30,000 applications annually for it’s job applications. She and her company have learned a great deal about the different skills that data scientists have.
So what are the different kind of data scientists in forestry? The post describes data scientists in forestry that typically focus in one of three areas: analytics, algorithms, or inference. It’s a reflection of my own observations across several forestry organizations.
The analytics-focused forest data scientist
The analytics-focused forest data scientist builds tools that are operational. Their roles within organizations are often focused and they are typically business-minded. While they may not have advanced degrees in statistics or data science, these professionals often see how data are integrated across an organization.
Analytics-focused data scientists also tend to be great communicators. In short, they have the ability to tell stories with data. They may have skills in data visualization, be strong writers, and may work across several teams in your forestry organization.
These data scientists are likely using programs like R, Excel, GIS, and Tableau on an everyday basis.
The algorithms-focused forest data scientist
The algorithms-focused forest data scientist develops tools that allow an organization to get the most out of their data. They may have a background in data science or computer science. Their roles within organizations are often designed so that they collaborate with the field crews that collect the data in the woods and the analysts that summarize the data in the office.
These professionals often lead efforts in data management and quality assurance within a company. They may also use data to perform machine learning tasks. A large part of their role is likely in helping others within their organization to use data effectively across diverse platforms. These platforms could include shared internal networks and cloud-based software.
These data scientists are likely using programs like R, Python, MS Access, SQL, and cloud computing services on an everyday basis.
The inference-focused forest data scientist
The inference-focused forest data scientist investigates causes and effects with an organization’s data. This kind of forest data scientist often has significant training in statistics and modeling and they likely have the word “biometrician” in their job title. They are often the first ones to analyze and model data after its been collected, and probably provided input into how the data were collected in the first place (e.g., by determining how much data to collect).
These professionals have perspectives that are often valued in a consulting role. They may also be an integral part of a company’s research and development efforts and may represent the company as a part of industry-university research cooperatives.
These data scientists are likely using programs like R and SAS on an everyday basis.
Conclusion
As the field of data science has grown, specialties have arose with individuals typically focusing on either analytics, algorithms, or inference. It’s unlikely that one person can handle all three data science roles within a company. (Those are unicorns, and unicorns only exist in my six-year-old nieces’ bedroom.) But in some forestry organizations, one person may fill two of these data science roles.
How do people in your organization fit within these data science roles? Email Matt with any comments.
–
By Matt Russell. Subscribe to our monthly email newsletter for data and analytics trends in the forest products industry.