How to Structure a Data Science Team: Key Models and Roles to Consider


People or organizations carefully following the trends and expert opinions in data science and predictive analysts will know that it is best to start from machine learning.  Experts often advise that it is best to take one step at a time. Start with the proverbial’ low hanging fruit’ and then move on to bigger and more complex operations as you gain relevant experience along the way.

Machine-learning-as-a-service (MLaaS) platform

Current trends and indications clearly point towards the value of machine-learning-as-a-service (MLaaS) platforms. Machine Learning is fast turning into a commodity thus making it well within the reach and resources of small and mid size organizations.  Leading vendors such as Microsoft, Amazon and Google provide Application Process Interfaces (APIs) and platforms to run basic ML operations without the elaborate need to invest in building complex infrastructure and hire professionals with deep knowledge and expertise in data analytics. It is a prudent move for organizations to take this lean and frugal approach in the beginning before gaining enough expertise to build and deploy your own analytics arsenal.

The question then arises is how to adopt and implement this incremental approach.  In the following section we discuss about data science team structure and level of complexity associated with them.

Data science team structures

Before you embark upon data science and predictive analytics, it is important that you are fully aware of how the initiative is going to be planned, introduced, implemented and further upped in terms of team structure.  There are three basic structures that can ease the process for you.

IT-centric structure

Owing to one reason or another, hiring a data analyst may not be the best option. Under such circumstances you can utilise the talent you have in-house with Chief Analytics Officer (CAO) assuming the lead role. Tasks like data preparation, creation of user interfaces, data development and model deployment can be efficiently handled by in-house IT time with the help of MLaaS solutions like Amazon Machine Learning.

Benefits of IT-centric structure

All the computational resources and infrastructure is provided and maintained by a third party vendor.
Option to train in house IT team members to leverage the power of predictive analytics is always available

Organizations can leverage new investments with existing IT resources
On the fillip side, the primary limitation of this model is that service providers offer very limited data cleaning procedures and machine learning methods.

Corporate training in data science from a premium online analytics institute can help you leverage its full potential without the restraints of limited resources or manpower by allowing to properly structure a data science team.

Location: Bengaluru, Karnataka, India

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