Lead Data Scientist (Analytics) - Digital Marketing
Grab is hiring a Lead Data Scientist (Analytics) - Digital Marketing. Level rates it ; you can apply on Grab.
Level's read on this job
- On Level's scale, this data scientist role is Uses AI ●●○○: an ordinary role that requires AI tools.
- Tools and skills the posting cites include scikit-learn.
- Level classifies it as a lead role.
- Level lists 436 other open roles at Grab.
- The role was posted within the past two weeks.
AI in this role
About Grab and Our Workplace
Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.
Get to Know the Team
The Growth Data Science team is a key component of our regional growth strategy. We focus on creating and implementing innovative approaches to drive growth through performance marketing automation, machine learning models, geospatial insights, user lifecycle and funnel optimisation, behavioural forecasting, and other advanced analytics methods. Our work spans across transportation, food, fintech, logistics, and platform services.
Get to Know the Role
We are seeking a Data Scientist with deep expertise in paid marketing measurement, AI-driven analytics, and operations to join our growing team. In this role, you'll collaborate with marketing leaders, analytics teams, and cross-functional stakeholders to solve complex digital marketing challenges using advanced measurement science, artificial intelligence, and data-driven approaches. You'll work on end-to-end paid marketing analytics initiatives, with opportunities to design and implement sophisticated attribution models, incrementality studies, media mix modeling (MMM), causal inference methodologies, and AI-powered tools that enhance reporting, measurement, and experimentation capabilities.
You will directly report to the Senior Manager, you will be based working onsite.
The Critical Tasks You Will Perform
- Partner with marketing stakeholders to align measurement strategies with business goals, channels, and data constraints.
- Select and apply attribution, incrementality, and causal inference methods, explaining trade-offs and recommendations.
- Design and run experiments—including A/B tests, holdouts, matched markets, and geo-lift studies—to measure campaign impact.
- Build and maintain Media Mix Models (MMM) to assess channel effectiveness, guide budget allocation, and forecast performance.
- Develop AI and machine learning solutions to improve campaign targeting, bidding, experimentation, and measurement workflows.
- Build scalable data pipelines integrating paid media, conversion, and internal data using SQL, Spark, and cloud technologies.
- Create analytics tools and dashboards to track campaign performance, acquisition costs, and marketing ROI.
- Conduct exploratory analysis and statistical modeling; validate models and translate findings into actionable recommendations.
- Manage project plans, document methodologies, and communicate insights clearly to technical and non-technical stakeholders.
- Collaborate across marketing, analytics, product, and engineering teams; mentor junior analysts and data scientists.
What Essential Skills You Will Need
- 6+ years in data science and analytics, including 3+ years in paid marketing measurement and operations.
- Expertise in attribution modeling, including multi-touch, algorithmic, and rule-based approaches.
- Experience designing incrementality studies, A/B tests, holdout analyses, and geo-lift studies.
- Strong background in Media Mix Modeling (MMM) or marketing mix optimization.
- Hands-on experience building, optimizing, and deploying machine learning models for marketing.
- Proficiency in Python, SQL, Pandas, Scikit-learn, and Spark; familiarity with causal inference tools such as CausalML, DoWhy, or EconML.
- Experience with cloud data platforms, marketing datasets, ad platform APIs, and ETL pipelines.
- Familiarity with generative AI and LLMs for automating insights and reporting.
Life at Grab
We care about your well-being at Grab, here are some of the global benefits we offer:
- We have your back with Term Life Insurance and comprehensive Medical Insurance.
- With GrabFlex, create a benefits package that suits your needs and aspirations.
- Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
- We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
- Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours
What We Stand For At Grab
We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
How we rate this
Lead Data Scientist (Analytics) - Digital Marketing at Grab rates 46 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.
Uses AI. An ordinary role that requires AI tools.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical data scientist job
- $167k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- What's a project where you used scikit-learn hands-on?
- This role expects you to use AI tools as part of the job. Which ones have you used, and for what?
- Tell me about a time an AI tool got something wrong. How did you catch it?
Adapt your resume
- List these exact terms on your resume: scikit-learn. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Put the AI tool in a bullet point about what you did, not just in a skills list. This role treats it as a required part of the job.
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