Aviv is a Staff Data Scientist at Intuit, previously a Lead Data Scientist at PayPal. She specializes in fraud prevention and cybersecurity, and in the past worked at the Prime Minister’s Office in the Cyber Security field, focusing on protocol analysis. Aviv graduated from Tel Aviv University with a double BSc in Computer Science and Life Science, while specializing in Bioinformatics.
Aviv is a Staff Data Scientist at Intuit, previously a Lead Data Scientist at PayPal. She specializes in fraud prevention and cybersecurity, and in the past worked at the Prime Minister’s Office in the Cyber Security field, focusing on protocol analysis. Aviv graduated from Tel Aviv University with a double BSc in Computer Science and Life Science, while specializing in Bioinformatics.
To be a successful Data Scientist in today’s world, you should have great business understanding (and preferably some domain knowledge), decent coding skills (in at least one language) and of course in depth theoretical and practical experience in multiple machine learning algorithms. Sounds like a handful?
In the past year or so, a new data scientist role is slowly emerging – the Full Stack Data Scientist. In practice, the main innovation is that on top of the above, the DS is expected to be proficient in MLOps, DevOps and production pipelines, with the goal being that a single data scientist can complete the project end to end – including deployment at scale and maintenance.
In this roundtable, I will share the evolution of the data scientist role with regards to this concept, from my personal experience over the past 5 years and share my thoughts on the challenges and opportunities of this direction. Finally, I will offer an alternative to this concept.
To be a successful Data Scientist in today’s world, you should have great business understanding (and preferably some domain knowledge), decent coding skills (in at least one language) and of course in depth theoretical and practical experience in multiple machine learning algorithms. Sounds like a handful?
In the past year or so, a new data scientist role is slowly emerging – the Full Stack Data Scientist. In practice, the main innovation is that on top of the above, the DS is expected to be proficient in MLOps, DevOps and production pipelines, with the goal being that a single data scientist can complete the project end to end – including deployment at scale and maintenance.
In this roundtable, I will share the evolution of the data scientist role with regards to this concept, from my personal experience over the past 5 years and share my thoughts on the challenges and opportunities of this direction. Finally, I will offer an alternative to this concept.
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