Lead Data Scientist (Analytics) – Digital Marketing

Role Overview

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.

What You Will Do

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.

Why It Might Be a Fit

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.

Requirements

• 5+ years of hands-on experience in data science and analytics, with at least 3+ years specifically focused on paid marketing measurement and operations
• Demonstrated expertise in attribution modeling methodologies (multi-touch, algorithmic, rule-based approaches)
• Proven experience designing and executing incrementality studies, A/B tests, and holdout analyses for paid marketing campaigns
• Strong background in Media Mix Modeling (MMM) or marketing mix optimization
• Experience designing and conducting geo-lift studies or other geographically-based causal inference studies
• Familiarity with causal Hands-on experience building and deploying machine learning models for marketing applications, including model optimization and performance tuning
• Proficiency in Python, SQL, and tools like Pandas, Scikit-learn, and Spark
• Working knowledge of cloud data platforms (e.g., AWS S3, Redshift) for managing large-scale marketing datasets
• Experience with marketing data sources and ad platform APIs (Google Ads, Facebook Ads, etc.)
• Manage data pipelines and ETL processes with a solid understanding of data engineering best practices
• Familiarity with statistical software or packages for causal inference (e.g., CausalML, DoWhy, EconML)
• Familiarity with generative AI tools and large language models (LLMs) for automating insights generation and reporting

Benefits

• Term Life Insurance
• comprehensive Medical Insurance
• GrabFlex
• Parental and Birthday leave
• Love-all-Serve-all (LASA) volunteering leave
• Grabber Assistance Programme
• FlexWork arrangements

Tagged as: Attribution Modeling, Data Analysis, Forecasting, Pipeline Management, Python