Grab — Singapore
Get to know our Team:
Grab’s Data Science Department works on some of the most challenging and fascinating problems in transport, economics, logistics, and space around. We apply machine learning, simulation, forecasting, scheduling, optimization, and different other advanced techniques to 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're looking for engineers and data scientists who bring fresh ideas from all various domains, including distributed computing, large-scale system design, security, artificial intelligence, reinforcement learning, natural language processing, and others. You will work on a specific project critical to Grab’s business with opportunities to switch between projects as you and our fast-paced business grow and evolve.
Expertise in following object-oriented languages: Scala, Python, Go (probably C++, and an eagerness to learn more).
Experience with both machine learning and building scalable production services.
Experience with distributed resource management systems (like Apache Mesos or Kubernetes).
Experience with distributed storage systems, including SQL or NoSQL (AWS DynamoDB, Cassandra).
Experience using machine learning libraries or platforms, including Tensorflow, Caffe, Theanos, Scikit-Learn, or ML Lib for production.
Understanding the basis from ML domain: bias-variance tradeoff, exploration/exploitation, different model families, like a neural net, decision trees, Bayesian models and deep learning algorithms.
Ability to solve business problems by applying machine learning methods.
Experience in stream processing area: Spark, Flink (or others).
You are a self-motivated, independent learner, and willing to share knowledge with team members, you pay attention to details and able to manage your time efficiently in a dynamic working environment.
Design solution architectures for applications that will use the machine learning (ML) models.
Take responsibility for ensuring that ML models and pipelines are deployed successfully into production, and troubleshooting issues that arise.
Deploy models and services on Kubernetes, including integration with a variety of services.
Automate model training and testing and deployment via machine learning continuous delivery pipelines.
Design and implement metrics to verify model and algorithm effectiveness.
Collaborate with other data scientists, software engineers, and business operation teams.
Report into the Data Science Dept