What constitutes a strong data science team?

No second thoughts on the fact that the world today is driven by data. This throws light on how critical has data science become. Well, clearly way more important than what was imagined. Also, organizations are relying on data science like never before to make better decisions. With this, how strong the team working on data science should need no special mention.

But, a data science team doesn't revolve around data scientists alone, as many tend to assume. There are many key players within this team who are involved in taking the business to a new height.

Data Science Team

A strong data science team of an organization if comprising of all the below-mentioned professionals will help the business in achieving laurels.

• Data scientist: This has to be one of the most obvious job roles for a strong data science team. Data scientists are entrusted with the job of solving business tasks using machine learning and data mining techniques. In addition to being skilled in mathematics, statistics, R, Python, etc., data scientists possess various soft skills as well that'd ultimately help the business to flourish.

• Data analyst: A data analyst ensures that all the collected data is relevant and exhaustive. Interpreting the analytics results is also one of the key duties performed. It is found that data analysts work separately i.e. with a team of data analysts or in line with the business and not with the data science team. However, if they're put to work with the same team as that of the data scientists, they can analyze the data, help make sure it's healthy and provide insights to the entire company.

• Machine learning engineer: A machine learning engineer does the job of combining software engineering and modeling skills. Determining which model to use and what data should be used for each model is the key objective they aim to achieve.

• Data strategist: There definitely has to be a link between the business and the data science team. This is where a data strategist comes in.

• Data architect: This is one of the most critical roles especially when there's a lot of data to handle. The responsibilities include warehousing the data, defining the database architecture, centralizing data, and ensuring integrity across different sources.

• Data engineer: Lack of a data engineer ultimately means there's no one to pay attention to architecture or code quality issues. Data engineers are involved with the tasks pertaining to implementing, testing, and maintaining infrastructural components that data architects design.

All of the above roles brought together to function as a team can work wonders for the organization. But the fact that coordination and understanding among all of them have to maintain to perfection cannot be neglected. Also, the results obtained by this team full of talent will have to be reported to either one or many of the CEO, COO, CFO, CIO, CTO, chief administrative officer (CAO), chief data officer (CDO), etc. depending on what the organization wants. Also, this varies from organization to organization. Now, many might question the need for reporting? Well, it does play a critical role in understanding whether the business needs have been fulfilled, what are the areas of improvement (if any), get feedback, etc.