Job description
Job Description:
Get to know our Team:
Grab’s Fulfilment-Dispatch Data Science team works on challenging and fascinating problems surrounding Grab’s allocation and batching capabilities - ensuring our passengers and eaters enjoy a high allocation and fulfilment rate.
A sample of problems we work on includes: intelligent allocation of drivers, grouping shared rides/orders, machine/deep learning-based predictions, online learning, on-demand routing and scheduling, and geospatial data mining.
We apply machine learning, geospatial and temporal data mining, simulation, forecasting, optimization, and many other advanced techniques on our huge datasets to push our business metrics to their bounds, directly and indirectly.
We foster a culture where we enjoy raising the bar constantly for ourselves and others, and that strongly supports the freedom to explore and innovate.
We are looking for candidates who are excited about working on challenging problems, applying their breadth and depth of specialised knowledge to design innovative solutions, and who push boundaries in seeking to continuously improve the growing suite of allocation and batching related services for our passengers, eaters, merchants and drivers.
Get to know the Role :
Find creative ways to solve passenger-driver allocation and batching problems optimally
Build complex and detailed simulations from ground up to dynamically model Grab’s operations
Drive product improvements and roll-out of new features
Build, deploy and own production-grade services
The day-to-day activities :
Deep dive into big data to conduct advanced statistical analyses
Design, build and productionize machine learning and optimisation algorithms efficiently
Integrate, simulate and A/B test the impact of algorithms and features
Store, retrieve and visualise results in a presentable manner that facilitates decision-making for rollouts
Effectively conceptualize analyses and communicate to business/product stakeholders
The must haves
Master’s degree (Ph.D. strongly preferred) in Computer Science, Electrical/Computer Engineering, Industrial & Systems Engineering, Operations Research, Mathematics/Statistics, Transportation Engineering, or related technical disciplines with 3+ years of DS work at a technology company; or equivalent experience
Strong Machine Learning fundamentals:
Experience in developing production-grade ML systems including exploratory analysis, feature engineering, hyperparameter tuning, creating data pipelines, etc.
Understanding of ML algorithms such as neural networks, SVM, decision trees, boosting techniques, reinforcement learning
Strong software development skills:
Excellent software development capabilities, preferably in Python; knowledge of GoLang would be an advantage
Familiar with Git-based source control, cloud-based development (AWS/Azure)
Experience with spinning up, deploying and maintaining microservices to serve DS/ML models
Strong working knowledge of Spark, MapReduce, SQL, NoSQL databases
Self-motivated and independent learner who is motivated to constantly learn from the team and from external reading; and willing to share knowledge with the team
Efficient and detail oriented time manager who thrives in a dynamic and fast-paced working environment
Really good to haves
Experience in working with geospatial/ mobility data
