We are excited to have Go-Jek as the first speaker for 2019! They will be sharing how ML is used in ride matching, scaling productions with ML Flows and how to manage burnouts in Data Science teams.
- 630pm - 7pm Networking
- 7pm - 830pm Talks by Gojek team
[Intro] Powering a SuperApp with Data Science by Maneesh Mishra (https://www.linkedin.com/in/drmaneeshmishra)
[Topic 1] How we use Machine Learning to match drivers and riders?
Speakers: Peter (https://www.linkedin.com/in/peter-richens/), Jawad (https://www.linkedin.com/in/mdjawad)
Abstract: Go-Jek, the Southeast Asian super-app, has seen explosive growth in both users and data over the past three years. Today the technology startup uses big data powered machine learning to inform decision-making in its ride-hailing, lifestyle, logistics, food delivery, and payment products. From selecting the right driver to dispatch, to dynamically setting prices, to serving food recommendations, to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning.
Our first Machine learning product for Go-Jek marketplace was a driver matching system, since then we have come a long way in improving and adapting to our growing business needs. In this talk, we will focus on Jaeger, our multi-objective machine learning allocation system.
Jawad is Data Scientist at Go-Jek, where he focuses on solving mission critical transportation and pricing problems for Southeast Asian markets. Jawad has wide-ranging experiences in Financial and Telecommunication sectors in the past. His academic work involves using simulation and data science tools to model construction workers' safety and productivity. Jawad holds a masters in Intelligent Systems Design from National University of Singapore.
Peter has been a Data Scientist at Go-Jek for two years, working on the matchmaking and pricing teams. He is a late starter in the world of technology; in previous lives, he worked for the UN and the government of Uganda. A recovering economist, Peter remains interested in causal inference and its use in combination with machine learning. He enjoys thinking long and slow about complex problems; he dislikes writing his own bio.
[Topic 2] Building complex machine learning flows for production
Speaker: Zhiling (https://www.linkedin.com/in/zhiling-chen-42764b90/)
It is often insufficient to run single machine learning models within a vacuum - we want to be able to leverage upon multiple models, perform data transformation, handle failure, among other things. Such a system involves a multiplicity of moving parts that can become extremely difficult to manage. This presentation will talk about Lasso, Go-Jek's lightweight service orchestration tool, and how it can be used to tie together the various components that make up a predictive unit, giving data scientists the latitude to build complex prediction flows while maintaining the ability to iterate quickly upon the system.
A machine learning engineer at Go-Jek, she and her colleagues work to scale ML for one of Southeast Asia's fastest growing apps. Her work aims to help Go-Jek's data scientists iterate faster, collaborate better, and serve up scalable, production ML solutions to meet the customers' needs.
[Topic 3] Burning out in Data Science
We address a tough topic: burning out as a data scientist. We explore reasons why data scientists experience burnout and discuss ways in which you can identify its symptoms, and discuss strategies to prevent and cure it.
Jireh is a data scientist who is passionate about helping other data practitioners succeed. At Facebook, Jireh built and maintained the AB Testing framework, Deltoid. He is currently at Go-Jek building organizational capacity for data scientists. He holds a patent for Messenger’s Sticker Search and has published in the Lancet on his favorite subject, Bayesian meta-analysis.