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Sovereign’s Capital
Sovereign’s Capital

Machine Learning Engineer - Trust and Fraud



Software Engineering, Accounting & Finance
Petaling Jaya, Selangor, Malaysia
Posted on Friday, July 5, 2024

Company Description

Life at Grab

At Grab, every Grabber is 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. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia.

Job Description

Get to know the team

The Trust, Identity, and Safety team acts as guardians of all our users on Grab. The data science team here leverage our rich datasets to find solutions to problems ranging from safety to fraud. We're a hands-on team interested in the end to end data lifecycle: from wrangling data to understanding the trade-offs between model complexity and deployment in production.

Get to know the job

We are looking for an experienced Machine Learning Engineer to help detect and reduce risk and fraud. If you're passionate about solving complex problems with immediate real-world impact, we want you!

The Day-to-Day Activities

  • Develop a deep behavioural 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, analytics and engineering teams to manage the entire end-to-end life cycle of designing, implementing, and deployment of models.
  • Work independently or in a team to solve complex problem statements
  • Think out of the box and innovate in all possible perspectives.


The must haves:

  • Bachelor's/Master's in Computer Science, Electrical/Computer Engineering, Industrial & Systems Engineering, Mathematics/Statistics, or related technical disciplines.
  • Proficient in programming in languages like Python, R, Java, or C++.
  • Proficient in algorithm design given various data structures including sparse matrices, sequences, trees, and graphs.
  • Strong working knowledge of machine learning including classification, clustering, and anomaly detection.
  • Experience in ETL, feature selections, hyper-parameter optimization, model validation and visualization.
  • Experience in tools like Scikit-Learn, Pandas, or XGBoost.
  • Experience in deep learning frameworks like Tensorflow or PyTorch.
  • Deep understanding and implementation experience of predictive modeling algorithms such as logistic regression, neural networks, forward propagation, decision trees and heuristic models, with familiarity dealing with trade-offs.
  • Experience in interfacing with other teams and departments to deliver impact solutions for the organization.
  • Self-motivated, independent learner, and enjoy sharing knowledge with team members.
  • Detail-oriented and efficient time manager in a dynamic and fast-paced working environment.

Really 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 in graph databases.
  • Experience in RNN/LSTM or Graph Neural Network is a plus.
  • Experience in Spark MLlib is a plus.

Additional Information

Our Commitment

We recognize that with these individual attributes come different workplace challenges, and we will work with Grabbers to address them in our journey towards creating inclusion at Grab for all Grabbers.

About Grab

Grab is the leading superapp platform in Southeast Asia, providing everyday services that matter to consumers. Today, the Grab app has been downloaded onto millions of mobile devices, giving users access to over 9 million drivers, merchants, and agents. Grab offers a wide range of on-demand services in the region, including mobility, food, package and grocery delivery services, mobile payments, and financial services across 428 cities in eight countries.

Join us today to drive Southeast Asia forward, together.