Bio
Soraya Hausl is a Senior Data Scientist at Booking.com where she uses machine learning and analytics to optimise the Search Ranking for both users and partners. Before that she was leading the Recommendations team at ASOS.com, helping users discover fashion items. She is passionate about building data products that improve customer experience. Soraya holds a MSc degree in Machine Learning from University College London (UCL) and has worked in strategy consulting.
Sarai Mizrachi is Data scientist at Booking.com in the personalisation track, use deep learning to develops destination recommendation in large scale. Before works as a data scientist developing recommendation system. Have MS.c in industrial engineering from Ben Gurion University, with a focus on machine learning.
Bio
Soraya Hausl is a Senior Data Scientist at Booking.com where she uses machine learning and analytics to optimise the Search Ranking for both users and partners. Before that she was leading the Recommendations team at ASOS.com, helping users discover fashion items. She is passionate about building data products that improve customer experience. Soraya holds a MSc degree in Machine Learning from University College London (UCL) and has worked in strategy consulting.
Sarai Mizrachi is Data scientist at Booking.com in the personalisation track, use deep learning to develops destination recommendation in large scale. Before works as a data scientist developing recommendation system. Have MS.c in industrial engineering from Ben Gurion University, with a focus on machine learning.
Abstract
Data science is still a relatively new and expanding field. In each company the role of a data scientist and teams structures are different. In our current and previous jobs we have experienced the opportunities and challenges of various setups.
In some, data scientists are “Full Stack Data Scientists” and are responsible for everything from business requirements during developing and implementing the model to deploying to production whereas in others they specialise on some elements of the data science project lifecycle. Furthermore they might be centralised in one team or embedded in cross-functional product teams across the organisation.
In this round table we will start by presenting the different “types” and roles of data scientists that exist and models of structuring data science teams. We will discuss the different experiences of participants. And we will share the different roles and team setup of data scientists at our company Booking.com.
Abstract
Data science is still a relatively new and expanding field. In each company the role of a data scientist and teams structures are different. In our current and previous jobs we have experienced the opportunities and challenges of various setups.
In some, data scientists are “Full Stack Data Scientists” and are responsible for everything from business requirements during developing and implementing the model to deploying to production whereas in others they specialise on some elements of the data science project lifecycle. Furthermore they might be centralised in one team or embedded in cross-functional product teams across the organisation.
In this round table we will start by presenting the different “types” and roles of data scientists that exist and models of structuring data science teams. We will discuss the different experiences of participants. And we will share the different roles and team setup of data scientists at our company Booking.com.
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 and Breakfast |
|---|---|
| 9:30 | Opening words by Shir Meir Lador, Data Science team lead at Intuit |
| 9:45 | Key Note - Diane Chang - Making Chatbot Magic |
| 10:15 | Key Note - Chana Greene - Virtual Reality data to product Reality |
| 10:45 | Talk - Hagit Grushka-Cohen Simulating user activity for assessing the effect of sampling on DB activity monitoring anomaly Detection |
| 11:15 | Break |
| 11:30 | Talk - Inbar Naor - Lessons from building deep learning recommendation systems |
|---|---|
| 12:00 | Talk - Hila Gonen - Gender Bias in Word Embeddings |
| 12:30 | Talk - Yonit Hoffman - Data to the Rescue! Predicting & Preventing Accidents at Sea |
| 13:00 | Lunch |
| 14:00 | Roundtable Discussions and Posters |
| 15:00 | Roundtable & Conference closure |
| 15:15 | Tutorial - Chae Young-Lee - Dealing with the Lack of Audio Data |
Planned Agenda
| 8:45 | Reception and Breakfast |
|---|---|
| 9:30 | Opening words by Shir Meir Lador, Data Science team lead at Intuit |
| 9:45 | Key Note - Diane Chang - Making Chatbot Magic |
| 10:15 | Key Note - Chana Greene - Virtual Reality data to product Reality |
| 10:45 | Talk - Hagit Grushka-Cohen Simulating user activity for assessing the effect of sampling on DB activity monitoring anomaly Detection |
| 11:15 | Break |
| 11:30 | Talk - Inbar Naor - Lessons from building deep learning recommendation systems |
| 12:00 | Talk - Hila Gonen - Gender Bias in Word Embeddings |
| 12:30 | Talk - Yonit Hoffman - Data to the Rescue! Predicting & Preventing Accidents at Sea |
| 13:00 | Lunch |
| 14:00 | Roundtable Discussions and Posters |
| 15:00 | Roundtable & Conference closure |
| 15:15 | Tutorial - Chae Young-Lee - Dealing with the Lack of Audio Data |
