Having only Data Scientists is not Enough
A data scientist is probably one of the hottest job titles these days. But there are many important skills that are required to build a useful data science solution/product. It is really challenging to find a data scientist with this kind of unicorn skill-set.
Successful organizations view data science as a team sport. They assemble individuals with different skill sets and assign them different responsibilities to support each step of the data science process.
While the demand for various data science roles is increasing by the day, people in industry have used the designations and descriptions a bit loosely. Hence, there is a lot of confusion around who does what in the industry.
The AI Hierarchy of Needs by Monica Rogati
Below are the roles and the contributions they should be making to ensure you’re producing quality outputs in the most efficient way possible:
Business (Data) Analyst: The first task from business is to frame the business problem & to define the scope, Business Analyst with data oriented skills helps with that.
Data Engineer: Once your team is aligned on the problem you’re trying to solve, the next step is to collect the raw data that will act as the foundation of your data model, basically Extract, Transform, and Load (ETL). Data Engineer builds these pipelines.
Data Scientist: Data Scientist applies algorithms and build models specifically chosen based on the use case your team has defined and the data that’s available. Apart from this, they productionize their findings by integrating them into your decision makers’ workflows.
Data Architect: When you are working on multiple data science use-cases, there will be situations when same data will be consumed by many use-cases & same tech-stack will be needed by many projects. Data Architect builds the platform to optimize the use of data & tech-stack.
Data Steward: If you are working on data science projects, data quality & data security are the major concerns to be addressed. Data Steward helps in managing & governing data sources & pipelines.
Analytics Manager: When multiple stakeholders/resources work on project/programmes, it becomes important to manage expectations, priorities & conflicts. Analytics Manager manages analytics or data science team.
UX Designer: UX Designers have the opportunity to think about how machine learning can be applied to improve their products in terms of personalizing experiences, detecting anomalies and providing insights.
Ankit Rathi is an AI architect, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture.