Get to know our Team:
Grab’s Data Analytics Department works on some of the most challenging and fascinating problems in transport, economics, logistics, payments, and the space around. We apply machine learning, simulation, forecasting, scheduling, optimization, and many other advanced techniques on our huge datasets to push our business metrics to their bounds, directly and indirectly. We foster a culture where we enjoy raising the bar constantly for ourselves and others, and that strongly supports the freedom to explore and innovate.
Get to know the Role:
- As the Head of Data Analytics, you will lead and build a team of data analysts to build out the lending & financial services business within GrabPay
- Explore and extract insights from massive dataset of geospatial, behavioural, economic, and the extraction of 3rd party data, to power a credit scoring model that can make financing decisions instantly, accurately and in fairness to the end customer
- Build Credit profile/score based on alternative data
- Leveraging data to help the business answer questions around further expansion, products, customer acquisition/retention/churn and any other business goals that can be addressed through data insights.
- Build, validate, test, and deploy machine learning or other data science models to predict and influence the behaviour of consumers with the goal of optimising system efficiency of the platform
- Develop a deep behavioral understanding and intuition of users and translate these intuitions into actionable, creative insights that produces heuristic or classification models to identify system violators
- 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
- Test and validate these insights via rapid experimentation and deployment
- Interface with business & operation teams to formulate solutions & product changes informed by your findings
The day-to-day activities:
- We are expanding into new territories and markets, advancing product offerings and creating entirely new ones leveraging big data. This requires the development of creative algorithims and enhancing models with deep machine learning
- Building and deployment of models within our existing ecosystem
- Managing the data environment and leading the team to build and shape a set of well-managed data streams
- Collaboration with other data scientists, software engineers, and business operation teams
The must haves:
- Advanced degree (MSc/PhD) in Computer Science, Economics, Electrical/Computer Engineering, Operations Research, or Mathematics/Statistics
- Experience in leading a team of data scientists / analysts preferably in a startup or a tech company
- Experience of developing credit scoring models/advanced predictive systems within FinTech, Finance, Insurance or Banking
- 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
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
- Experience in Scala or PySpark on distributed systems
- Familiarity with Python Scikit Learn, Panda or Spark ML/Mllib is a plus
