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.
The Growth team at Grab strategizes and executes on step-changing growth initiatives through data-driven approaches and rapid A/B testing, and owns the conception, execution, and experimentation of products, features and engineering systems that drive this mandate.
This position reports into the Data Science department.
Get to know the Role:
- Develop a deep understanding and intuition of our users’ interests and behavior
- Translate these intuitions into actionable, creative insights that produce heuristics or statistical models to identify opportunities for sustainable rapid growth
- Test and validate these models via rapid deployment and experimentation
- Manage and own the entire end-to-end lifecycle of model design, integration with engineering, and maintenance
- Interface with business and operation teams to formulate solutions and product changes informed by your findings
The must haves:
- Strong understanding and implementation experience in predictive modeling algorithms such as logistic regression, neural networks, and decision trees, including familiarity dealing with the trade-offs between model performance and business needs
- Proficiency wrangling data from SQL or NoSQL sources and performing statistical modeling in languages like Python, Scala, Java, R, or Julia
- Ability to collaborate with other teams and functions to deliver impactful solutions to the organisation
- An attitude of self-motivation, effective time management, and the ability to operate in a dynamic and fast-paced working environment
Really nice to haves:
- Previous experience as a growth hacker in a startup or tech company
- Experience building distributed ML pipelines using Spark/Hadoop
- Knowledge in graph databases, NLP, Deep Learning (CNN, RNN/LSTM), or graphical models (HMM, CRF)
- Recent programming experience in a production environment
