CRM & Data Analytics Analyst

Get to know our Team:

Grab's Marketing Data Science Department works on some of the most challenging and fascinating problems in Transport, Food, Fintech, logistics, economics, and the space around. We apply Machine Learning, deep learning, geospatial data mining, forecasting, optimization, and many other advanced techniques on our huge datasets across Rides, Food and Fintech business to impact our business metrics. 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.

Sample of problems the Marketing Data Science Department solve - Complex Customer Lifecycle (Spread across Temporal behaviour, RFM, Geo-Spatial, User Level Persona) Recommender Systems, machine/deep learning - based predictions (all sorts of pro-active and re-active churn models), Customer LifeTime Value (CLTV), Supply/demand forecasting for cross-pollinating market places, Incentives and promotions optimization, etc. Our team identifies and solves real-time and large-scale Marketplace Marketing problems using a combination of multiple Data Science techniques. 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:

• Conceptualise and develop machine learning models to Support Customer lifecycle Analytics across Rides, Food and Fintech

• Incorporate models to analyse and predict customer behaviour and commercial impact on the overall ecosystem

• Drive Customer engagement, product improvements and confidence using advance data science techniques

• Conceptualise Machine Learning framework and architecture to address core marketing challenges such as Channel Behaviour Analytics (Including SMS, Push, In-App, Email, Social), User Onboarding, Customer churn, Cross sell, Upsell

• Deep dive into data to conduct Business Insights, advanced statistical analysis and incorporate machine learning and optimisation 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

• Manage multiple concurrent projects and drive them to successful completion

• Communicate problem formulation, solution, analyses and insights to team members and stakeholders

• 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 Mathematics, Operations Research, Data Science, Industrial / Systems Engineering, or Computer Science, with specialization in Machine Learning, User Behaviour or Optimization techniques

• Minimum 2 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 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