Get to know the Team:

Grab is leading a consortium for Digital Bank license and build a bank with the right foundation - using data, technology and trust to solve problems and serve customers. We have big dreams to unlock and financial inclusion for people in our region is just one. If you have what it takes, help build our new Digibank with us.

The Day-to-Day Activities

• Develop predictive models to forecast customer behaviour, product adoption, and other relevant metrics to drive business value.

• Analyse data using statistical techniques to uncover insights and trends in customer behaviour, product performance, and market dynamics.

• Collaborate with cross-functional teams to define business problems, identify data requirements, and develop solutions.

• Design and implement experiments to test hypotheses and optimise business processes.

• Develop data visualisations and dashboards to communicate insights to business stakeholders.

• Monitor and maintain data quality and integrity, ensuring that data is accurate and up-to-date.

• Stay current with emerging trends and technologies in data science, digital banking, and financial services industries.

The Must-Haves

• Bachelor degree in Analytics, Data Science, Mathematics, Computer Science, Statistics or related fields.

• Minimum of 5 years of experience in data science, machine learning or related fields.

• Strong knowledge of programming languages such as Python, R, or SQL

• Experience with data visualisation tools such as Tableau, PowerBI, or similar

• Analytical and independent problem solver. Meticulous with high attention to detail.

• Strong communicator with ability to switch hats between data/technical speak and business/layperson speak.

• Ability to lead and mentor junior data scientists.

The Nice-to-Haves

• Experience working in the digital banking or financial services industry.

• Experience with big data technologies such as Hadoop, Spark, or similar.

• Familiarity with cloud computing platforms such as AWS, Google Cloud, or Azure.

• Experience with natural language processing (NLP) or computer vision (CV) techniques.

• Knowledge of deep learning frameworks such as TensorFlow, Keras, or PyTorch.

• Experience with A/B testing and experimentation methodologies.