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
8:45 | Reception |
---|---|
9:30 | Opening words by Shir Meir Lador, Data Science leader at Intuit |
9:45 | Yael Karov - AI For Assisting in Task Completion |
10:15 | Ofra Amir - Agent Strategy Summarization: Describing Agent Behavior to People |
10:45 | Break |
11:00 | Lightning talks |
12:30 | Lunch & Poster session |
---|---|
13:30 | Roundtable session & Poster session |
14:30 | Roundtable closure |
14:45 | Gal Yona - How Fair Can We Be |
15:15 | Daphna Weissglas - Turning Data Science Into Precision Medicine Empowering Millions |
15:45 | Closing remarks |
Planned Agenda
8:45 | Reception |
---|---|
9:30 | Opening words by Shir Meir Lador, Data Science leader at Intuit |
9:45 | Yael Karov - AI For Assisting in Task Completion |
10:15 | Ofra Amir - Agent Strategy Summarization: Describing Agent Behavior to People |
10:45 | Break |
11:00 | Lightning talks |
12:30 | Lunch & Poster session |
13:30 | Roundtable session & Poster session |
14:30 | Roundtable closure |
14:45 | Gal Yona - How Fair Can We Be |
15:15 | Daphna Weissglas - Turning Data Science Into Precision Medicine Empowering Millions |
15:45 | Closing remarks |