Data Scientist (Trust and Fraud) Job Opportunity at Grab

Join Grab, Southeast Asia's leading superapp, and be a part of a company dedicated to improving the lives of millions across the region. With our comprehensive suite of everyday services, including deliveries, mobility, financial, and enterprise services, we strive to create economic empowerment for the people of Southeast Asia. Guided by The Grab Way and its core principles—Heart, Hunger, Honour, and Humility—we work tirelessly to make every aspect of life better for all.

About the Role: Data Scientist (Trust and Fraud)

The Trust, Identity, and Safety team at Grab acts as guardians of all our users. Leveraging our rich datasets, our data science team tackles problems ranging from safety to fraud. We're a hands-on team that manages the entire data lifecycle: from wrangling data to balancing model complexity and production deployment.

We are seeking an experienced Data Scientist to help detect and reduce risk and fraud. If you're passionate about solving complex problems with immediate real-world impact, we want you!

Key Responsibilities

• Develop a deep behavioral understanding and intuition of our users from data to identify emerging fraud trends.
• Develop and improve machine learning models to detect risk and fraud.
• Collaborate with product, risk, compliance, and engineering teams to manage the entire end-to-end lifecycle of models from design and implementation to deployment.
• Work independently or in teams to solve complex problem statements.
• Innovate and think out-of-the-box in all possible perspectives.

Qualifications

Must-Haves

• Master's degree in Computer Science, Electrical/Computer Engineering, Industrial & Systems Engineering, Mathematics/Statistics, or related technical disciplines.
• Proficient in programming languages like Python, R, Java, or C++.
• Strong working knowledge of machine learning including classification, clustering, and anomaly detection.
• Experience in ETL, feature selection, hyper-parameter optimization, model validation, and visualization.
• Experience with tools like Scikit-Learn, Pandas, and XGBoost.
• Experience with deep learning frameworks like TensorFlow or PyTorch.
• Deep understanding and experience with predictive modeling algorithms.
• Ability to interface with other teams and departments to deliver impactful solutions.
• Self-motivated, independent learner, and enjoy sharing knowledge with team members.
• Detail-oriented and efficient in time management in a dynamic and fast-paced environment.

Nice-to-Haves

• Deep understanding of the fraud space with hands-on knowledge of fraud, payments, and risk, especially on tech products.
• Recent programming experience in a production environment.
• Experience with graph databases.
• Experience with RNN/LSTM or Graph Neural Network.
• Experience with Spark MLlib.

Benefits at Grab

We prioritize your well-being and offer a comprehensive range of benefits globally:

• Term Life Insurance and comprehensive Medical Insurance to protect your loved ones.
• GrabFlex, allowing you to customize a benefits package suited to your needs and aspirations.
• Maternity and Paternity Leave to help you embrace new life and create lasting memories.
• Confidential Grabber Assistance Programme for guidance and support through life's challenges.
• Wellbeing@Grab initiatives, including health programs, webinars, and vibrant carnivals.
• FlexWork arrangements to help you achieve a harmonious work-life balance.
• Localized benefits tailored to each country—ask your recruiter for more details during your interview.

Our Commitment to Diversity and Inclusion

Grab is committed to building an inclusive and equitable workplace that empowers diverse Grabbers to perform at their best. As an equal opportunity employer, we consider all candidates fairly, irrespective of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments, and other unique attributes. If you require accommodations to fully participate in the recruitment process, please include your request(s) when applying.

Together, we spread opportunities by bringing together diverse perspectives. It's not a box