Data Scientist (Growth)

Get to know our Team:

Grab's Data Science Department works on some of the most challenging and fascinating problems in transport, logistics, economics, and the space around. We apply deep learning, geospatial data mining, simulation, forecasting, scheduling, 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.

Sample of problems the Data Science Department solve - Intelligent allocation, machine/deep learning - based predictions (all sorts!), Dynamic pricing, Supply/demand forecasting and positioning, Incentives and promotions optimization, Carpooling, Shuttle and on-demand bus routing and scheduling, Multi-modal transport, Geospatial data mining, etc.

This position reports into the data science department.​

Get to know the Role:

• Develop a deep understanding and intuition for growth strategies, acquisitions, and process optimization

• Translate these intuitions into actionable, creative insights that produces heuristic or classification models to identify opportunities for sustainable rapid growth

• Manage and own the entire end-to-end lifecycle of designing models, working with Engineering for implementation, to maintenance and enforcement

• Interface with business and operation teams to formulate solutions and product changes informed by your findings

• Work independently or with other teams to solve complex problem statements

The day-to-day activities:

• Test and validate these insights via rapid experimentation and deployment

The must haves:

• Proficient in RDBMS such as PostgresQL or MySQL; and statistical programming in languages like R, Python, Java, C++ or SAS

• Experience in ETL, feature selections, modeling, model validation and conducting data analyses using R, SQL, Python or any JVM languages

• Strong understanding and implementation experience of predictive modeling algorithms such as logistic regression, neural networks, forward propagation, decision trees and heuristic models, with familiarity dealing with trade offs between model performance and business needs

• Experience in interfacing with other teams and departments to deliver impact solutions for organisation

• 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:

• Previous experience as a growth hacker in a startup or tech company is a plus

• Experience in geospatial databases or graph databases

• Recent programming experience in a production environment

• Experience in Scala or PySpark on distributed systems

• Interest in working with MapReduce technologies (such as Hadoop / Spark)

• Familiarity with Python Scikit Learn, Panda or Spark ML/Mllib is a plus

• Experience with AWS stack