Thanks for the clarity Rahul. Specially regards to roles and responsibilities. Just some follow up questions
We also see the role for Data Engineers these days where they take large amounts of structured and mostly unstructured data, apply software engineering and ML magic to convert it into data that can be consumed by the resarchers/applied schientists etc. How and where do they fit in ?
As per your article, can we say Researchers/Applied Researchers == Data Scientists ? How would you relate/differentiate, as specially in India I see the role of Data Scientists being very prominent.
Data engineering is also a pretty helpful role and exists in a lot of companies. I would say they would fit on before an MLE/Applied Scientist even starts their work. Mostly they would create the data infrastructure including data pipelines, ETL processes, and data warehouses/lakes. This might blend in a little bit with the ML Platform Engineer
role but I see this role as mostly a non-ML role and more as a SWE role as it primarily involves software engineering principles applied to data systems.
As per my experience, the Data Scientist role in India is very akin to ML engineering apart from deployment and data pipeline creation. You would create models as a data scientist in India but will not do everything end to end. I mean you would have other developers help in procuring the data, deployment, and maintenance-related stuff. You just manage the model mostly. This is my general observation about the role and it is also changing in India nowadays and is pretty company-specific.For me at least the most similar role that I could find outside as a DS in India was MLE.
However, the Data Scientist role outside India differs greatly from what we call a DS in India. Mostly the data scientists outside are much more connected to Business. Their main work is to manage Hypothesis testing and define business metrics along with the business stakeholders. As such their basic tech stack involves SQL, Dashboards, and docs.
I hope that clears some things up. Its always a moving target to define these roles fully.
Thanks for the clarity Rahul. Specially regards to roles and responsibilities. Just some follow up questions
We also see the role for Data Engineers these days where they take large amounts of structured and mostly unstructured data, apply software engineering and ML magic to convert it into data that can be consumed by the resarchers/applied schientists etc. How and where do they fit in ?
As per your article, can we say Researchers/Applied Researchers == Data Scientists ? How would you relate/differentiate, as specially in India I see the role of Data Scientists being very prominent.
Thanks for the comment, Gaurav.
Data engineering is also a pretty helpful role and exists in a lot of companies. I would say they would fit on before an MLE/Applied Scientist even starts their work. Mostly they would create the data infrastructure including data pipelines, ETL processes, and data warehouses/lakes. This might blend in a little bit with the ML Platform Engineer
role but I see this role as mostly a non-ML role and more as a SWE role as it primarily involves software engineering principles applied to data systems.
As per my experience, the Data Scientist role in India is very akin to ML engineering apart from deployment and data pipeline creation. You would create models as a data scientist in India but will not do everything end to end. I mean you would have other developers help in procuring the data, deployment, and maintenance-related stuff. You just manage the model mostly. This is my general observation about the role and it is also changing in India nowadays and is pretty company-specific.For me at least the most similar role that I could find outside as a DS in India was MLE.
However, the Data Scientist role outside India differs greatly from what we call a DS in India. Mostly the data scientists outside are much more connected to Business. Their main work is to manage Hypothesis testing and define business metrics along with the business stakeholders. As such their basic tech stack involves SQL, Dashboards, and docs.
I hope that clears some things up. Its always a moving target to define these roles fully.
Thanks Rahul. Appreciate the time and efforts you made just to answer this.