Senior Data Scientist (TIS - Reinforcement Learnin...

Job description

Job Description:
Get to know our 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.
We are looking for an experienced Data Scientist to join our Trust data science team, where AI technologies are developed to prevent fraud loss in Grab’s all business lines including food and grocery delivery, ride hailing, financial service such as payment, etc.
The candidate is expected to work on reinforcement learning and optimization algorithms in a very sophisticated fraud detection system in order to make the system even more intelligent and auto-adaptive.
If you’re passionate about solving complex problems with immediate real-world impact, we want you!
Get to know the role:
  • Develop reinforcement learning algorithms taking a wide range of signals as input during the user journey and estimate the optimal decision, given a variety of constraints.
  • Idealize, design, implement, experiment, and iteratively refine the algorithms and models.
  • Collaborate with product, risk, and operation teams to understand, define and solve complex problem statements.
  • Work with fellow data scientists and engineers to manage the entire end-to-end life cycle of designing, implementing, and deployment of models.
  • Think out of the box and innovate in all possible perspectives.
The must haves:
  • Ph.D. or 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 knowledge of math, optimization, and operation research.
  • Deep understanding and implementation experience of reinforcement learning algorithms.
  • Strong working knowledge of machine learning including classification, clustering, and anomaly detection.
  • Experience in deep learning frameworks like Tensorflow or PyTorch.
  • 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.

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