What is a full stack data scientist?

A full stack data scientist, as the name suggests, is one who has working experience in all facets and departments of Data Science – starting from data extraction & harmonization, devising statistical models and machine learning algorithms on the data, visualizing it in a readable format & finally presenting it in a consumable format to business. Generally, a potential full stack data scientist should have hands-on experience on diverse tools & technologies such as R (or Python), software engineering (ETL), machine learning (NLP algorithms etc.) and Tableau (or Qlikview).

A full-stack data scientist is someone who can play the role of Data Engineer, Data Modeler and Data Analyst together. There is no standard definition on this issue, but this is how I think of it.

Data Scientist is a catch all term for at least four distinct roles:

  • Data Theorists come up with the fundamental algorithms for artificial intelligence or other aspects of data science, like Deep Learning or Calibrated Quantum Mesh.
  • Data Engineers/ Architects use these algorithms and build the models that would lead to business results in any situation.
  • Data Modelers use these models, integrate with the right kind of data and run the models to get business outcomes.
  • Data Analysts gather and cleanse the relevant data to run the models.