Inbal
Horev

Data Science Workflows
Gong

Inbal
Horev

Data Science Workflows

Gong

Bio

Inbal is a lead data scientist at Gong where she works on a wide range of problems in NLP and statistical modeling. Before joining Gong, she received the MEXT scholarship to perform machine learning research at Tokyo University. Inbal holds an M.Sc in computer science from the Weizmann Institute of Science and a B.Sc in physics & electrical engineering from the Technion where she focused on computer vision and high-dimensional statistics.

Bio

Inbal is a lead data scientist at Gong where she works on a wide range of problems in NLP and statistical modeling. Before joining Gong, she received the MEXT scholarship to perform machine learning research at Tokyo University. Inbal holds an M.Sc in computer science from the Weizmann Institute of Science and a B.Sc in physics & electrical engineering from the Technion where she focused on computer vision and high-dimensional statistics.

Abstract

Data science projects vary greatly in scope and structure. And, while there isn’t a single way of conducting such projects, they often share similar workflows. For example, every project must go through an integration phase, whether it’s carried out by the data scientist herself or by handing the integration off to an engineer. Project workflows are full of decisions regarding complex interfaces (programmatic and organizational) and resource assignment (human and computational).

 

Using Gong’s research projects as a basis for our discussion we’ll sketch a typical project workflow, describe the various stages, and suggest additional ones if necessary. We’ll identify the potential bottlenecks and inefficiencies and propose ways to overcome them. At the end of the day, how do we ensure that we deliver a relevant product as smoothly as possible and in the required time frame?

Abstract

Data science projects vary greatly in scope and structure. And, while there isn’t a single way of conducting such projects, they often share similar workflows. For example, every project must go through an integration phase, whether it’s carried out by the data scientist herself or by handing the integration off to an engineer. Project workflows are full of decisions regarding complex interfaces (programmatic and organizational) and resource assignment (human and computational).

 

Using Gong’s research projects as a basis for our discussion we’ll sketch a typical project workflow, describe the various stages, and suggest additional ones if necessary. We’ll identify the potential bottlenecks and inefficiencies and propose ways to overcome them. At the end of the day, how do we ensure that we deliver a relevant product as smoothly as possible and in the required time frame?

Planned Agenda

Planned Agenda