How to spot a “citizen data scientist” on your team

Two data experts recently shared in a Harvard Business Review article: “We sometimes ask companies, ‘Which one would you prefer?’ Almost all opted for the latter.

A well-qualified data scientist has become a “rare commodity” today because they are rare and hard to find for any organization. Enter citizen data scientists, who not only bridge the gap between “real” data scientists and the business, but also perform their self-service analytics.

Today, many companies identify the skills available internally, cultivate them and create a “citizen data scientist” from an unqualified person. Joao Tapadinhas, research director at Gartner, says, “Most organizations don’t have a lot of data scientists to work across the enterprise, but they do have a lot of skilled information analysts who could potentially become citizen data scientists.

So who are “citizen data scientists”?

Gartner defines a citizen data scientist as “a person who works to generate patterns using advanced diagnostic analytics or predictive and prescriptive capabilities; however, this person’s primary work would be outside of statistics and analysis. »

The position of “citizen data scientist” is unusual because it can only be created through an internal promotion model. Although the title exists, you hardly find any job postings for employers looking for a “citizen data scientist”. This position is primarily created for the employee(s) in the organization who have the skills of a data scientist but not the qualification.

Saurabh Moody, the founder of Alphaa AI, says a citizen data scientist is the link between the technical side of analytics and business. “Training in it means empowering business users to make data-driven decisions every time and realize the larger motive of connecting data to dollars. They champion interpreting KPIs to target north star metrics, analyzing using the right tools, and communicating insights to achieve business goals.

Is a “citizen data scientist” job worthwhile?

The creation of an exclusive position of “citizen data scientist” is one of the solutions to address the current shortage of data scientists. Data scientist jobs deal with mundane operational tasks such as validating data quality, merging datasets, and identifying data sources. Hiring an “expensive” data scientist to perform these tedious and time-consuming tasks is not cost effective. It is best to have someone work on it in combination with automation, to reduce the costs involved.

Find citizen data scientists within the organization

A citizen data scientist should have similar characteristics to a traditional qualified data scientist; For starters, one might look for people with the following traits:

Familiar with data and their relationships

· Great problem solving skills

Can think outside the box

· Curiousity

· Rigor

Careful not to jump to conclusions

Although non-technical, the elements listed above form the basis of a data-driven role. Also, sometimes you will come across people who have all the necessary traits but lack the qualifications. In such cases, the following review approach can be taken to ensure that the individual is capable of handling data projects.

  • Award small data science projects to assess their eligibility.
  • Consider hosting a hackathon or dev camp focused on a fun data science problem.
  • Kaggle provides great use cases for events like these. These are great ways to both identify and initiate a training program.

Once hired, citizen data scientists can be part of well-thought-out programs and growth strategies to ensure progress in the right direction. Here are some examples:

  • Recognize people with high analytical potential and offer them training and development assignments.
  • Use of business intelligence/autoML tools for maximum efficiency.
  • Detect AI biases and create model trust and transparency standards so that citizen data scientists can establish Explainable AI (XAI) systems.
  • Ensure that citizen data scientists don’t feel like they’re swimming against the tide.
  • Reward imaginative and innovative approaches to solving traditional business problems.
  • Recognize, encourage and reward citizen data scientists for their contributions.

What Makes Someone a Good Citizen Data Scientist

Here are some of the skills organizations are looking for in citizen data scientists:

  • Organizational Context: Must know the goals of the business vision and understand how data can help achieve those goals.
  • Divergent Thinking: Should have broader thinking skills and skills to create data patterns and connect beyond the comprehension of an ordinary employee.
  • Strong Analytical Skills: Must have analytical skills to undertake complex data analysis work.
  • Ability to Evaluate Information Meaningfully: Must be able to pick up details missed by others and provide meaningful conclusions after properly reviewing data.
  • Emphasize business value: To carve out a niche in their current role, citizen data scientists need to showcase their data analytics work.
  • Industry adjacency: The best candidates for citizen data scientists work in data science, which involves a lot of math and analytics.

For those in industry who aspire to pursue a career in data science but are unwilling or unable to return to formal education for a degree, this is an attractive option for furthering your skills in the following areas with courses and apply for the position of citizen data scientist. :

  • Learn Python, R programming, Tableau, etc.
  • Take courses on business intelligence and its branches, such as data mining and descriptive analysis.
  • Take courses in big data analysis.

The role of “citizen data scientist” is becoming increasingly attractive to business leaders and business organizations as data-driven developments have influenced nearly every industry. This seems like a strong case for closing the talent gaps in the data industry.

Sean N. Ayres