Data Science Lead
4 days ago
About Grab
We're Southeast Asia's leading superapp, and our mission is to drive the region forward by economically empowering everyone. We do this by harnessing the power of technology and AI to make life easier for our users.
The Country & Marketing Analytics team plays a crucial role in helping us achieve this goal. They analyze our past operations and marketing programs to understand their impact on our business, brand, and users. This helps us improve future projects and make data-driven decisions.
As a Data Science Lead, you'll report to the Regional Marketing Analytics Manager and work closely with the Marketing and Growth teams. Your primary objective will be to define and implement regional strategies aimed at expanding our user base and enhancing user engagement.
You'll leverage advanced analytics techniques to derive deep consumer insights, optimize workflows, and enhance productivity within the analytics team and across stakeholder groups. You'll also collaborate with stakeholders to design experiments that shape the future of our marketing & growth strategy.
Critical Tasks You Will Perform
- Propose innovative business ideas by diving deep into data and addressing pressing customer challenges.
- Provide data-driven recommendations to support strategic decision-making.
- Influence key metrics and design experiments to shape our marketing & growth strategy.
- Drive significant improvements across our platforms, enhancing user experience and contributing to our mission of excellence.
Essential Skills and Qualifications
- A minimum of 6 years of analytical experience combined with business acumen.
- The ability to help solve complex problems, narrow down multiple options, and take ownership of open-ended ambiguous business problems.
- Expertise in data storytelling and creating intuitive dashboards using PowerBI or Tableau.
- Fluency in SQL, Python, R, or other scripting/programming languages, and comfort with large datasets and ETL processes.
- Experience with machine learning techniques and algorithms, applied to real-world problems.
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