Get to know our Team:
Grab's Marketing Data Science department works on some of the most challenging and fascinating Marketing problems. This team supports Grab at group level which includes Transportation, Food, Fintech, and space around. We apply all the way from Foundational descriptive statistics to Machine Learning, deep learning, geospatial data mining, forecasting, optimization, and many other advanced techniques on our huge data sets to impact our business metrics.
Our team identifies and solves real-time and large-scale Marketplace Marketing problems using a combination of multiple Data Science techniques. Sample of problems the Marketing Data
Science Department solve - Complex Customer Lifecycle (Spread across Temporal behaviour, RFM, Geo-Spatial, User Level Persona) Impact measurement , machine/deep learning - based predictions (all sorts of pro-active and re-active churn models), Customer Lifetime Value (CLTV) for cross-pollinating marketplaces etc. We build, validate, test, and deploy models and algorithms using proven and experimentation techniques. We are looking for scientists who are passionate about data and want to apply advanced AI/ML techniques to solve real-world problems. This position reports into the Marketing data science department
Get to know the Role:
- Able to execute independent as well as group initiatives and communicate problem formulation, solution, analysis, and insights to wider team members and stakeholders
- Conceptualise Machine Learning framework and architecture to address core marketing challenges such as Channel Behaviour measurement (Including SMS, Push, In-App, Email, Social), User Onboarding, Customer churn, Cross-sell, Upsell
- Conceptualise and develop machine learning models to Support Customer lifecycle Analytics across Rides, Food and Fintech
- Deep dive into data to conduct Business Insights, advanced statistical analysis and incorporate machine learning and optimization algorithms and simulate their impact on the overall system
- Develop and execute necessary analyses or A/B tests to validate Experiments, models, and perform detailed analysis to identify improvement opportunities
- Effectively conceptualize analyses and present across business stakeholders and country marketing teams. Work independently or in a team to solve complex problem statements
- Build, validate, test, and deploy models and algorithms using proven and experimental techniques
- Define hypotheses, develop and execute necessary tests, experiments, and analyses to prove or disprove them
The must haves:
- Masters Degree or Ph.D. in Economics, Mathematics, Operations Research, Data Science, Interdisciplinary Engineering, Computer Science, with specialization in Machine Learning, User Behaviour or Optimization techniques.
- Bachelors degree with strong experience in related marketing area can also apply
- Minimum 2-4 years of relevant post-degree experience in solving large-scale complex problems, especially in Online Marketplaces, transport or logistics business
- Proficient in traditional RDBMS Such as SQL, Writting ETL jobs, and No-SQL database systems; programming languages like R, Python, SAS; and distributed computing platforms like Hadoop and Spark
- Good knowledge in Supervised, Unsupervised and Reinforcement learning/algorithms
- Detail-oriented and efficient time manager who thrives in a dynamic and fast-paced working environment
- 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
- Develop and execute necessary tests and analyses to validate models, and perform detailed analysis to flag out vulnerabilities and improvement opportunities
- Visualise simulation results in a manner that facilitates the required analyses
- Able to present complex subjects clearly and coherently to non-domain experts
Really good to have:
- Experience in working with Digital/Mobile Marketplaces with Customer lifecycle data, geospatial and mobility data
