Or Basson &
Sheer Dangoor

Keeping the Code Clean for a Data Scientist

Intuit

Or Basson
Sheer Dangoor

Or Basson & Sheer Dangoor

Keeping the Code Clean for a Data Scientist

Intuit

Or Basson
Sheer Dangoor

Bio

Or is a Data Scientist at Intuit, the global leader in financial management software, she works on fraud prevention models.
Before joining Intuit, Or worked 7 years as an officer in the intelligence forces of the IDF. She worked as a data scientist on anomaly detection models and as a full-stack senior software engineer. Or is a “Mamram” engineering course graduate, holds an MBA (graduated with honors) and a B.Sc. in Computer Science from The College of Management.

 

Sheer is a Data Scientist at Intuit, the global leader in financial management software. She works on fraud prevention models.
Before joining Intuit, Sheer worked at the Weizmann Institute of Science as a data scientist, leading on-demand projects including collaboration with top Israel universities. Sheer holds an M.Sc. in Brain Science from the Weizmann Institute of Science and a B.Sc. in Biology and Cognition with honors from the Hebrew University of Jerusalem.

Bio

Or is a Data Scientist at Intuit, the global leader in financial management software, she works on fraud prevention models.
Before joining Intuit, Or worked 7 years as an officer in the intelligence forces of the IDF. She worked as a data scientist on anomaly detection models and as a full-stack senior software engineer. Or is a “Mamram” engineering course graduate, holds an MBA (graduated with honors) and a B.Sc. in Computer Science from The College of Management.


Sheer is a Data Scientist at Intuit, the global leader in financial management software. She works on fraud prevention models.
Before joining Intuit, Sheer worked at the Weizmann Institute of Science as a data scientist, leading on-demand projects including collaboration with top Israel universities. Sheer holds an M.Sc. in Brain Science from the Weizmann Institute of Science and a B.Sc. in Biology and Cognition with honors from the Hebrew University of Jerusalem.

Abstract

Many times a DS project starts with a proof of concept, most of us will start with quick & dirty code that can get the job done. When the project accelerates and expands, usually you find yourself in a tight schedule – the research phase is over and when you are about to ship to production, you have to deal with many problems that make you feel sorry that you didn’t write your code more production friendly.

 

We wish to open a discussion on the ongoing debate of Clean Code concept for data scientists; How to maintain a clean code standard? What are the differences in code writing between research and production? Should we have code conventions? How do we manage a shared code? We would like to answer these questions from a data scientist point of view.

 

We will also share lessons learned from our experience as data scientists at Intuit, and our previous experience (Or as a senior software engineer and Sheer as a researcher).

Abstract

Many times a DS project starts with a proof of concept, most of us will start with quick & dirty code that can get the job done. When the project accelerates and expands, usually you find yourself in a tight schedule – the research phase is over and when you are about to ship to production, you have to deal with many problems that make you feel sorry that you didn’t write your code more production friendly.

 

We wish to open a discussion on the ongoing debate of Clean Code concept for data scientists; How to maintain a clean code standard? What are the differences in code writing between research and production? Should we have code conventions? How do we manage a shared code? We would like to answer these questions from a data scientist point of view.

 

We will also share lessons learned from our experience as data scientists at Intuit, and our previous experience (Or as a senior software engineer and Sheer as a researcher).

Discussion Points

  • Data scientist role definitions – full stack data scientists vs. specialisations
  • Pure data science teams vs embedded teams
  • Data science reporting lines
  • Professional and personal development in embedded teams

Discussion Points

  • Data scientist role definitions – full stack data scientists vs. specialisations
  • Pure data science teams vs embedded teams
  • Data science reporting lines
  • Professional and personal development in embedded teams

Planned Agenda

Planned Agenda