CRM Data & Analytics Senior Manager

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