Lead/Senior Data Scientist (Machine Learning for Maps)

The Team 

Grab — the leading super app in Southeast Asia — combines transport, food delivery, logistics, payments, and much more in a single platform. In all this, our map data and infrastructure serve as fundamental building blocks in enabling multiple location-based services. 

The world in Southeast Asia constantly changes — and therefore, so must our representation of this world. This not only provides a huge challenge, but also an exciting opportunity to leverage high-volume, high-velocity datasets in various modalities to build complex models that help Grab understand this world intelligently. 

The Data Science (Machine Learning for Maps) team at Grab focuses on building map-based intelligence such as travel time estimation, traffic forecasting, routing, Place-of-Interest (POI) search and data curation, and positioning. Our work powers various Grab services like transport allocation, logistics, and pricing. We extensively use computer vision, natural language processing (NLP), and information retrieval along with conventional machine learning methods on a variety of signals including images, videos, text, sensor readings, and GPS probes to understand places and road networks. We also support the development of innovative, highly-scalable models through advanced research in order to delight our customers with intelligent products. 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.

Duties and Responsibilities

• Understand business needs, identify areas for investigation, translate them to technical problems to be solved

• Build quick model prototypes (e.g. prediction, forecasting, clustering) using both proven and experimental techniques

• Define hypotheses, develop and execute necessary tests, experiments, and data analyses to prove or disprove them

• Translate data speak to human speak by effectively conceptualizing analysis to team members and business stakeholders

Requirements

• Ph.D. or Master’s in Computer Science, Electrical/Computer Engineering, Operations Research, or Mathematics/Statistics..

• Understanding of machine learning, deep learning, data mining, algorithmic foundations of optimization, probability and statistics.

• Proficient in one or more of the following programming languages: Python, R, Scala, Golang.

• Desired skills - Sound knowledge of ML/DNN algorithms,  hands-on experience using modern ML/numerical frameworks (e.g. TensorFlow, PyTorch, Spark ML). 

• Familiar with relational databases and hands-on experience with SQL

• Self-motivated, independent learner, and willing to share knowledge with team members

• Detail-oriented and efficient time manager in a dynamic working environment

• Able to communicate well in English both verbally and in written communication, as well as convey data insights and results with effective visualisations.

Nice-to-Haves

• 3+ years of industry experience in a Computer Science, Engineering, Operations Research or Mathematics/Statistics capacity

• Experience in curating, manipulating and analysing large geographical or spatio-temporal datasets is a very desirable plus. 

• Expertise in one or more specialized domains including information retrieval, computer vision, natural language processing, text mining, and geo-spatial data mining.   

• Experience in processing data and building ML models at scale using distributed computing frameworks (e.g. Spark).

• Experience in production software engineering (web development, test-driven development, code versioning with Git, code reviews, CI/CD) is a big plus.

• Experience in designing, deploying or maintaining ML systems in production using Docker/Kubernetes is a big plus.

• Familiarity with modern data pipeline and warehousing stacks, e.g. Hive, Livy, Azkaban/Airflow, PrestoDB, Redshift, Kafka stream processing etc.

• Familiarity with NoSQL frameworks or search engine / indexing frameworks like ElasticSearch

• Familiarity with GIS frameworks and/or databases (e.g. PostGIS).

• Familiarity with cloud platforms like AWS or Azure