Get to know our Team:
Grab’s lending business (GFSA – Grab Financial Services Asia) is a recent addition to Grab’s array of product and service offerings focused on extension of Micro credit to drivers, agents and merchants in Grab’s ecosystem. GFSA team is a combination of strong talent pool in its Regional Hub (Singapore) and deep local market operators across its focus markets. We are incredibly excited about the opportunity ahead of us. We are looking to put together the best possible combination of business build drive, industry expertise and local market depth as part of our team. GFSA team is responsible for end to end conceptualisation, design, development, execution and ongoing management of all lending activities in its focus markets and segments.
- Develop a deep behavioural understanding and intuition of our drivers, agents and merchants to build predictive models for credit risk, customer management, collections, purchase propensity, fraud, identity and many other business needs within Grab Financial.
- Manage and own the entire end-to-end lifecycle of building and validating predictive models along with their deployment and maintenance.
- Interface with business, risk & operation teams to formulate solutions & product changes informed by your findings and business inputs/reality.
- Work independently or in a team to solve complex problem statements.
- Individual contributor role with 2-8 years of experience. Candidates will be aligned appropriately within the organization depending on experience and depth of knowledge.
- The day-to-day activities: Build predictive models using a mix of machine learning and traditional analytics methods.
- Validate models on new datasets, based on in-market performance.
- Engineer predictive features from internal data assets to build refined customer profiles. Identify external data assets to bring into the model mix.
- Solve previously unsolved analytics problems using best in class data analytics and machine learning methodologies.
- Work backwards to conceptualise and design analytic model frameworks to solve business problems.
- Build and maintain dashboards for model performance KPIs.
The Must Haves:
- Expert in building machine learning and predictive models in Python and Spark.
- Coding and modelling skills in Spark, Python, Java. Others like SAS, R good to have.
- SQL, Presto, Hive proficiency.
- Sound knowledge of machine learning concepts. Illustrative machine learning concepts/methods are: Bagging, Boosting, Regularisation, Online Learning, Recommendation Engines etc.
- Sound knowledge of statistical modelling methods. For e.g. CHAID CART, Regressions, SVD, PCA etc.
- Experience on text analytics stack is a plus: NLP, parts of speech tagging, word2vec etc.
- Expert in feature creation on a variety of data types.
- Professional experience in building machine learning analytics model development
- Understanding of trade-offs between model performance and business needs.
- Strong problem-solving mindset is critical for success in this role.
- Self-motivated, independent learner, and enjoy sharing knowledge with team members
- Work experience and knowledge of more than one domain is a plus - Risk Analytics, Marketing Analytics, Telecom analytics, Retail analytics, Fraud analytics etc.