Grab — Singapore
Get to know our Team:
GrabPay (digital payment wallet for SEA) is a recent addition to Grab’s array of product and service offerings focused on the extension of Microcredit to drivers, agents, and merchants in Grab’s ecosystem.
GFSA team is a combination of a strong talent pool and deep local market operators across its focus markets. We are incredibly excited about the opportunity ahead of us.
We are looking to put together the best possible combination of business build drive, industry expertise, and local market depth as part of our team.
GFSA team is responsible for end to end conceptualization, design, development, execution and ongoing management of all lending activities in its focus markets and segments.
More About the role:
As a Machine Learning Engineer, Data Services, you will be working on managing all aspects of Data Science, from Model selection, feature engineering and validation, deployment, and insights; You would also be actively involved in the core ML platform build-out, here at Grab, in conjunction with other related internal teams. The team works closely with Data Engineering, Product Management and Analytics, Legal, Compliance and business stakeholders across the SEA in understanding and tailoring the offerings to their needs. As a member of the Data Services, GrabPay, you will be an early adopter and contributor to various open source big data technologies and you are encouraged to think out of the box and have fun exploring the latest patterns and designs in the fields of Machine Learning and Software Engineering.
Roles & responsibilities:
Build, validate, test, and deploy machine learning models (e.g. predictive, forecasting, clustering) using proven and experimental techniques. Deploy an online learning model where applicable
Define hypotheses, develop and execute necessary tests, experiments, and analyses to prove or disprove them
Translate data speak to human speak by effectively conceptualizing analysis to team members and business stakeholders
Develop creative algorithms by employing machine learning, and data mining techniques
Contribute to team’s innovation and IP creation
Help build next-gen Data Science lifecycle management suite of tools/frameworks, including ingestion, tagging, feature stores, deployment and A/B testing
Lead technical discussions across the organization through collaboration, including running RFC and architecture review sessions, tech talks on new technologies as well as retrospectives
Apply core software engineering and design concepts in creating operational as well as strategic technical roadmaps for business problems that are vague/not fully understood
Experience & qualifications:
Ph.D. graduate, or Masters(with at least 3 years of experience), in Computer Science, Electrical/Computer Engineering , Operations Research or Mathematics/Statistics
Understanding of machine learning, deep learning, data mining, algorithmic foundations of optimization
Experience with machine learning framework (scikit-learn, Spark MLlib etc)
Proficient in one or more of the following programming languages: Python, R, Scala.
Experience in building ML models at scale, using real-time big data pipelines on platforms such as Spark/MapReduce
Familiar with noSQL, stream processing and distributed computing and messaging platforms
Strong software engineering background, with good knowledge of algorithms, distributed systems, databases and software engineering.
“Educated” on latest developments in the areas of dev-ops and CI/CD, including containerization, blue-green deployments, 12-factor apps, secrets management etc
Self-motivated, independent learner, and willing to share knowledge with team members
Detail-oriented and efficient time manager in a dynamic and dynamic working environment