Grab - the leading super app in Southeast Asia - combines transport, food delivery, logistics, payments, and much more in a single platform. The transport department runs the ride hailing business and makes sure everyone enjoys the convenience of exploring the cities.
The Transport data science team builds AI models and systems to optimize user experience in the whole trip. We extensively use data mining and machine learning technologies on a multimodality dataset of users’ whole lifecycle. With the recovery of mobility demands after the lock down, there is huge upside of the business and great opportunity for innovation in the area of transport.
Get to know the role:
Develop algorithms to identify patterns from data and derive insights of user profiles.
Build machine learning models to predict user behaviors on Grab app.
Continuously improve, optimize and deploy algorithms and models in production.
Generate novel ideas, perform experiments and work with product and engineering teams throughout the iterations.
Document, present and communicate ideas, results and learnings.
Coach and mentor junior members.
The must haves:
Ph.D. or Master’s in Computer Science, Electrical/Computer Engineering, or related technical disciplines.
Proficient in programming in languages like Python, R, Java, or C++.
Proficient in algorithm design given various data structures including sparse matrices, sequences, trees, and graphs.
Strong working knowledge of machine learning including classification, clustering, and anomaly detection.
Experience in ETL, feature selections, hyper-parameter optimization, model validation and visualization.
Experience in tools like Scikit-Learn, Pandas, or XGBoost.
Experience in deep learning frameworks like Tensorflow or PyTorch.
Deep understanding and implementation experience of predictive modeling algorithms with familiarity dealing with trade-offs.
Experience in interfacing with other teams and departments to deliver impact solutions for the organization.
Self-motivated, independent learner, and enjoy sharing knowledge with team members.
Detail-oriented and efficient time manager in a dynamic and fast-paced working environment.
Really nice to haves:
Deep understanding of personalization technologies with hands-on experience in developing various recommenders.
Recent programming experience in a production environment.