Two data experts recently shared in a Harvard Business Review article, “We sometimes ask companies, ‘Which would you rather have: a newly minted PhD data scientist or 20 people who can conduct basic analyses in their current jobs?’ Almost all opted for the latter.”

A well-qualified data scientist has become a “rare commodity” today as they are sparse and difficult for any organisation to find. Enter citizen data scientists, who not just bridge the gap between “true” data scientists and the business but also perform their self-service analytics.

Today, many companies identify the skills available in-house, nurture them and create a “citizen data scientist” out of a non-qualified person. Joao Tapadinhas, research director at Gartner, says, “Most organisations do not have many data scientists to work across the business, but they do have plenty 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 in generating models using advanced diagnostic analytics or predictive and prescriptive capabilities; however, that person’s primary job would be outside the field of statistics and analytics.” 

The “citizen data scientist” position is unusual as it can be created only through an in-house promotion model. Although the title exists, you hardly find any employment listings for employers seeking a “citizen data scientist.” This position is mainly created for that employee/employees in the organisation who has 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. “To train for it means enabling one to help business users make data-driven decisions every time and achieve the larger motive of connecting data to dollars. They champion the interpretation of KPIs to target north star metrics, analysing through the right tools, and communicating analytics to meet business objectives.”

Does a ‘citizen data scientist’ post help?

Creating an exclusive “citizen data scientist” position is one of the solutions to address the present shortage of data scientists. Data scientists’ jobs deal with mundane operational tasks like validating data quality, merging data sets and identifying data sources. Having an “expensive” data scientist perform these tedious and time-consuming tasks is not cost-effective. It is preferred to get someone to work on this in combination with automation, to reduce the incurred costs. 

Finding citizen data scientists within the organisation

A citizen data scientist must have traits as that of a traditional qualified data scientist; for a start, one could look for people with the following traits:

· Familiar working with data and their relationships

· Great problem-solving skills

· Can think outside of the box

· Curiosity

· Thoroughness

· Cautious to not jump to conclusions

Though not technical, the above listed form the base for a data-oriented role. Further, sometimes you would come across individuals who carry all necessary traits but don’t possess the qualifications. In such cases, the following examination approach can be taken to ensure the person is capable of handling data projects.  

• Assigning small data science projects to assess their eligibility.
• Consider hosting a hackathon or development camp focusing on a fun data science problem. 
• Kaggle provides great use cases for events like these. These are great ways of both identifying and kick-starting a training programme.

Once employed, citizen data scientists can be part of well-thought-out programs and growth strategies to ensure headway in the right direction. The following are a few examples: 

• Recognising people with high analytical potential and providing them with training and developmental assignments.
• Use of business intelligence/autoML tools for maximum efficiency.
• Detecting AI biases and creating model trust and transparency standards so that citizen data scientists can establish explainable AI (XAI) systems.
• Making sure citizen data scientists do not feel like they are swimming against the ‘business as usual’ tide. 
• Reward imaginative and innovative approaches to address traditional business issues.
• Recognising, encouraging and rewarding citizen data scientists for their contributions.
  

What makes one a good citizen data scientist 

Following are certain skills that organisations look for in citizen data scientists:

• Organisational context: Should know the company’s vision objectives and understand how data can assist in achieving those goals.
• Divergent thinking: Should have broader thinking ability and skills to create data models and connect beyond an ordinary employee’s comprehension.
• Strong analytical skills: Must have analytical skills to take up complex data analysis work.
• Ability to assess information meaningfully: Must be able to catch details missed by others and provide significant conclusions after examining the data properly. 
• Emphasise business value: To carve a niche from their current role, citizen data scientists must highlight their data analysis 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 the industry aspiring to build a career in data science but unwilling or unable to go back to formal education for a degree, it is a worthy option to upskill in the following areas with courses and apply for the position of a citizen data scientist: 

• Learning Python, R Programming, Tableau, etc.
• Taking classes on business intelligence and its branches, such as data mining and descriptive analytics.
• Taking up big data analytics courses. 

The “citizen data scientist” role is becoming increasingly enticing to corporate leaders and business organisations as data-driven developments have influenced nearly every sector. It seems like a solid filing for the talent gaps in the data industry.