GO-JEK

Data Scientist - Matchmaking

Job description

Location

Singapore

Work Type

Permanent

Application Posted

July 7, 2021

About the Role

As our Data Scientist, you'll be an integral player in the Marketplace Data Science team based in Singapore. With the latest cutting-edge data science tech at your disposal, you'll focus your efforts on bringing our Marketplace systems (i.e. supply, demand, and pricing) to the next level. Employing various quantitative techniques such as Machine Learning, Optimization, Simulation, and Bayesian Techniques, you'll drive asymmetric values for our businesses at Gojek. You'll be heavily involved in ideation, research, and building prototypes, and the folks in the Data Science Platform will bring your models to production. Your efforts will directly influence the stability and scalability of Gojek's Marketplace stream, and thus to company's top and bottom line as a whole.

What You Will Do

  • Work with Data Scientists, Machine Learning Engineers, and Business users to build, deploy, and scale Data Science solutions on match-making, supply, pricing problems in Gojek that touch the company’s baseline
  • Use your experience in Data Science, Machine Learning, Software Engineering, distributed systems to develop these systems, and work with the platform team to take the systems to production
  • Work with Business teams to continuously refine and improve the systems to cater to ever-changing Gojek needs
  • Design and develop world class Data Science solutions to enhance the current stack of Marketplace algorithms for supply, demand, pricing

What You Will Need

  • At least 4 years of experience as a Data Scientist or Machine Learning Engineer, with experience in Python, Golang/Java, and Unix
  • A Bachelor's Degree in Computer Science, Statistics, Operations Research, or a relevant quantitative field
  • Solid knowledge of Data Science and Machine Learning fundamentals, with proven experience formulating Data Science solutions to business problems
  • Proven ability to recognize business needs and to communicate with multiple stakeholders within the Product Management, Business and Operations teams
  • Experience in taking Data Science models to production
  • Prior academic or industry work experience with forecasting methods like auto-regressive models, Markov models, and Kernel-based methods
  • Prior experience with simulations for modeling the stochastic nature of marketplace supply and demand, and knowledge of the transportation and mobility space
About the Team
Our Data Science Platform team, a subsidiary of the Marketplace Data group, is a family of engineers based in Singapore and Bengaluru. Our main responsibility is to build data products for ride-hailing, logistics, food delivery, and payment. How does one achieve this, you may ask? By leveraging Machine Learning; we design and manage a Machine Learning platform to accelerate ML product development and simplify operations at Gojek. From selecting the right driver to dispatch, to dynamically setting prices, to forecasting real-world events, to processing the hundreds of millions of orders per month across more than 20 products - all this is driven by Machine Learning. We are working on several projects now, including one about how we can strategically position our driver-partners to improve our customer experience.
Our team is unique in its charter as an enabler of Data Science and Machine Learning systems at production scale within a hyper-growth deacorn. Some of our creations include open-source MLops technologies such as Feast (our feature store), Merlin (our model deployment platform), and Turing (our experimentation platform), and internal tools such as Clockwork. Our products are used in mission-critical parts of Gojek’s product offerings, such as dynamic pricing, driver allocation, food recommendations, and fraud prevention. We constantly work with machine learning practitioners to solve high-impact issues, and generalize the solution at a platform level to benefit the organization as a whole.
We are a tight-knit group made up of exercise-buffs, amateur gamers, and (wannabe) whiskey connoisseurs. We work hard and play hard, and believe it or not, we actually enjoy each other’s company!

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