Senior Data Scientist (Grab Financial Group, Risk)

Get to know our Team:

The Grab Financial Risk team acts as guardians of risk 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 to detecting money laundering. We’re a hands on team interested in the end to end data lifecycle: from wrangling data to understanding the tradeoffs between model complexity and deployment in production. If you’re passionate about solving complex problems with immediate real-world impact, we want you!

Get to know the Role:

• Develop a deep understanding and intuition of our transactions payment transaction, customer behavioral and other unique data sets collected by Grab, especially in the space of how they would defraud Grab and our customers

• Manage and own the entire end-to-end life cycle of designing models, working with Engineering for implementation, to maintenance and enforcement and test and validate these insights via rapid experimentation and deployment

• Interface with business & operation teams to formulate solutions & product changes informed by your findings, work independently or in a team to solve complex problem statements

• Work on a wide range of projects, from credit card fraud prediction, identify anti-money laundering networks, to image recognition for ID card tampering; All across a wide range of technical fields, including deep learning, graph algorithms and real time predictions

The must haves:

• Proficient in RDBMS such as PostgresQL or MySQL and able to write SQL optimized SQL queries well

• Proficient in one data science friendly programming languages like R, Python or Scala

• Experience in ETL, feature selections, modeling, model validation and conducting data analyses using R, SQL, Python or any JVM languages

• Good understanding of predictive modeling algorithms such as logistic regression, neural networks, forward propagation, decision trees and graph based models, with familiarity dealing with trade offs between model performance and business needs

• Experience in interfacing with other teams and departments to deliver impact solutions for organisation

• 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

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, with experience in deploying algorithms into production, working on tracking and A/B testing setup and reporting metrics to business stakeholders

• Experience in distributed systems, e.g. hadoop, spark (scala or pyspark) etc

• Experience with either Tensorflow or Pytorch deep learning framework, with experience optimizing deep learning models