Lead Data Scientist (Analytics)

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Get to Know the Team

As a User-Centric Analytics (UCA) team member, your role involves understanding, analyzing, and predicting the behavior of users (passengers, drivers, and merchants). This knowledge is critical in driving user engagement strategies, enhancing user experience, and contributing to overall business growth.

Get to Know the Role

As a senior or lead data scientist, we expect you to be an individual contributor who leads the project end-to-end, from gathering requirements and bridging the gap, to building and deploying a model, and delivering impact for various teams. The primary responsibilities for this role include building user-level prediction models for Competitive Index and High Net Worth Individuals, which require an advanced understanding of statistics and AI/machine learning. As a data scientist specializing in user-level prediction models, your role will be crucial in optimizing user engagement and retention, which are key strategies for driving business growth and profitability. You will report to Data Analytics Manager and based in Jakarta office.

The Critical Tasks You Will Perform

• Develop and implement predictive models to understand and forecast user behavior and engagement. This involves utilizing various machine learning and AI, as well as statistical modeling techniques.
• Analyze and interpret massive or sparse datasets of transactional, behavioral, and millions of user interactions on our platform to understand user behavior and trends that can inform business strategies.
• Continually improve user-level models through iterative enhancements, ensuring they remain effective and relevant.
• Ensure the scalability of models to handle larger datasets or more features.
• Collaborate cross-functionally with software engineers, product managers, marketing, and country teams to translate business needs into data solutions.

What Essential Skills You Will Need

• A degree in Computer Science, Software Engineering, Mathematics, Physics, or other related fields.
• 4 to 5+ years of experience in large-scale Machine Learning systems, with a background in Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related field.
• Familiarity with the concept of MLOps Lifecycle, which refers to the process of managing and automating the lifecycle of machine learning models, including data preparation, model training, model evaluation, and model serving.
• Proficiency in ML Modeling with ML Frameworks such as Tensorflow and PyTorch.
• Proficiency in Causal Inference or other statistical methods, and Python.
• Hands-on experience with one or more big data processing frameworks such as Spark.

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

• We have your back with Term Life Insurance and comprehensive Medical Insurance.
• With GrabFlex, create a benefits package that suits your needs and aspirations.
• Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
• We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
• Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For At Grab

We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.