Senior Data Scientist (Integrity)

Company Description

About Grab and our workplace

Grab is Southeast Asia’s leading superapp. We are dedicated to improving the lives of millions of users across the region by providing them everyday services such as deliveries, mobility, financial services, enterprise services and others. More than that, we provide the opportunity for them to have a better life. And that aspiration starts inside Grab because we believe in a seamless blend of work and home life, making every aspect of life better for all.

Guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles—the 4Hs: Heart, Hunger, Honour and Humility—we work to create economic empowerment for the people of Southeast Asia. With our unwavering commitment to our values, we believe that we're more than a service provider; we're agents of positive change.

Job Description

Get to know our Team

The Grab Financial Risk & Compliance team acts as guardians of risk and compliance for all the grab financial products within Grab. The data science team leverages our rich datasets to find solutions to problems ranging from payment risk prediction with sequence based models, detecting money laundering with graph algorithms, to image recognition for automated ID verification. Our team is also at the forefront of researching new methods to stay ahead of emerging fraud tactics, contributing to the creation of intelligent and secure products.

The Day-to-Day Activities

• Analyze transactional data to understand the patterns and trends of fraudulent activities, using statistical and machine learning techniques.
• Collaborate with business stakeholders to comprehend the operational impact of fraud and translate business needs into analytical requirements.
• Develop hypotheses around fraudulent behavior, design and execute experiments, tests, and data analyses to validate these hypotheses.
• Create, train, and deploy scalable machine learning models for transaction monitoring and real-time fraud detection.
• Evaluate model performance using appropriate metrics and datasets, ensuring high accuracy and efficiency in fraud identification.
• Deploy and maintain fraud detection solutions in production environments, continuously monitoring their effectiveness and making improvements as needed.
• Contribute to the team's innovation efforts and intellectual property creation in the realm of payment fraud detection.
• Stay up-to-date with the latest research and advancements in Graph Neural Networks, Sequence / Natural Language Processing / LLMs.
• Work closely with other data scientists, software engineers, product managers, and the financial operations team to integrate fraud detection systems seamlessly into Grab's platform.

Qualifications

Must-have:

• 4+ years experience as a data scientist or PhD in computer science, physics, stats, or related quantitative fields.
• Strong Python, SQL, and relevant programming skills. Familiarity with numeric libraries, containers, and modular software design.
• Experience with running end-to-end data science workflow for production environment, e.g. knowledge of putting models into batch prediction and scheduling (like Airflow/Kubeflow)
• Experience with standard machine learning libraries like Tensorflow / Pytorch, XGBoost / LightGBM, Sklearn.
• Good knowledge of state-of-the-art DNN architectures and machine learning techniques and algorithms (graph neural networks, diffusion models etc..)
• Good written and oral communication skills. Strong teamwork and interpersonal skills.
• Self-motivated and independent learner, detail-oriented and efficient time manager in a dynamic and fast-paced working environment

Good-to-have:

• Experience with distributed, large data processing like pyspark.
• Experience with developing DNN architectures like GNNs or sequence models.
• Experience working on tracking and A/B testing setup and reporting metrics to business stakeholders
• Usage of graph databases and graph neural networks in production environments
• Experience with stream data processing (e.g. flink)

Additional Information

Benefits at Grab:

We care deeply about your well-being and are committed to supporting you every step of the way. Here are some of the global benefits we offer:

• Protect and provide for your loved ones with peace of mind, knowing we have your back with Term Life Insurance and comprehensive Medical Insurance.
• Craft a benefits package that suits your unique needs and aspirations with GrabFlex, because we believe in empowering you to thrive.
• Embrace the magic of new life and create lasting memories with your family through Maternity and Paternity Leave.
• Life can be overwhelming, but you're never alone. Our confidential Grabber Assistance Programme is here to guide and uplift you and your loved ones through life's challenges.
• Your well-being is our priority. Benefit from our holistic well-being initiatives through Wellbeing@Grab, including health programmes, informative webinars, and vibrant carnivals.
• Achieve a harmonious work-life balance with our FlexWork arrangements, allowing you to adapt and thrive in your personal and professional life.

We’ve got many different benefits hyper localised in each country. Speak to your recruiter during your interview to find out more.

What we stand for at Grab:

We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique. If you require accommodations to fully participate in the recruitment process, you are encouraged to include your request(s) when applying.

We deliver the greatest impact and ideas when we bring together diverse perspectives. It is what enables us to spread opportunities to Grabbers and our partners. It’s not a box-ticking exercise; it’s who we are.