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Lead Data Scientist (User Trust)
Grab   via JobTech

Get to know our Team:
The User Trust team acts as guardians of all our users on Grab. We 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 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:

  • Lead and build a team of Data Scientists in User Trust
  • Develop a deep behavioral understanding and intuition of our passengers and drivers, especially in the space of how they would violate our policies and game our systems
  • Translate these intuitions into actionable, creative insights that produces heuristic or classification models to identify and take down those who violate our Terms of Services
  • Manage and own the entire end-to-end lifecycle of designing models, working with Engineering for implementation, to maintenance and enforcement
  • Work independently or in a team to solve complex problem statements

The day-to-day activities:

  • Test and validate these insights via rapid experimentation and deployment
  • Generate multivariate statistical models to identify latent factors, preventive and preemptive capabilities that the trust framework requires
  • Interface with business & operation teams to formulate solutions & product changes informed by your findings

The must haves:

  • Depending on your experience, you will be considered either for the Lead Data Scientist or the Senior Data Scientist role
  • Experience in leading a team of data scientists, preferably in a startup or a tech company
  • Proficient in RDBMS such as PostgresQL or MySQL; and statistical programming in languages like R, Python, Java, C++ or SAS
  • Experience in ETL, feature selections, modeling, model validation and conducting data analyses using R, SQL, Python or any JVM languages
  • Strong 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 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

Really nice to haves:

  • Good understanding of the fraud space with hands-on knowledge of fraud, payments and risk, especially on tech products
  • Experience in geospatial databases or graph databases
  • Recent programming experience in a production environment
  • Experience in Scala or PySpark on distributed systems
  • Interest in working with MapReduce technologies (such as Hadoop / Spark)
  • Familiarity with Python Scikit Learn, Panda or Spark ML/Mllib is a plus