Senior Data Scientist (Dispatch)Singapore
Life at Grab
At Grab, every Grabber is guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles - the 4Hs: Heart, Hunger, Honour and Humility. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia.
Get to know our Team:
Grab’s Fulfilment-Dispatch Data Science team works on challenging and fascinating problems surrounding Grab’s allocation capabilities - ensuring our passengers and consumers enjoy a reliable fulfilment rate.
Some of the problems we work on include: efficient matching of drivers to orders and trips, statistical/machine/deep-learning-based predictions, offline and online learning of contextual allocation parameters, and geospatial data mining.
We apply machine learning, spatio-temporal data mining, simulation, forecasting, optimization, and other advanced techniques on our high-volume datasets to drive optimal business outcomes 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 to work on challenging problems, who can apply their breadth and depth of knowledge to design innovative solutions, and who push boundaries in improving the growing suite of allocation-related services for our passengers, eaters, merchants and drivers.
Get to know the role:
Find creative ways to solve passenger-driver allocation problems optimally
Build and integrate models to hi-fidelity simulators which models Grab’s operations, along with conducting simulations to guide better product decisions and rollouts.
Drive product improvements and roll-out of new features, including automation and self-optimizing of configs utilized in allocation related services.
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
Experience with relational and/or NoSQL databases, and strong working knowledge of SQL / Spark / streaming.
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, detail-oriented time manager who thrives in a dynamic and fast-paced working environment
Nice to have:
Experience in working with geospatial/mobility data
We are committed to building diverse teams and creating an inclusive workplace that enables all Grabbers to perform at their best, regardless of nationality, ethnicity, religion, age, gender identity or sexual orientation and other attributes that make each Grabber unique.
Grab is an equal opportunity employer. We owe our success to the talents of our globally-diverse team and the varying perspectives they add to our thriving community.
Grab does not accept unsolicited resumes sent by recruiting agencies. Please do not forward resumes to our job postings, Grab employees or other parts of the business. Grab will not be liable to pay any fees to agencies for candidates hired as a result of unrequested resumes.