Data Scientist (Analytics) - Consumer Experience

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Job Summary

Job Type


Years of Experience
Information not provided

Tech Stacks
Python SQL Azure PowerBI Databricks R Analytics

Job Description

Company Description

Life at Grab

At Grab, every Grabber is guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles - the 4Hs: Heart, Hunger, Honour and Humility. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia.

Job Description

Get To Know Our Team

At Grab, we're committed to revolutionizing the consumer experience across our platform in Southeast Asia. Our Consumer Experience Team spearheads efforts to enhance user growth, retention, and engagement through innovative strategies and data-driven insights. We're seeking a dynamic Data Scientist (Analytics) to join our team, focusing on product experimentation, A/B testing, and deep dive research to drive impactful improvements in user experience.

Get To Know The Role

  • Product Experimentation and A/B Testing:
    • Collaborate closely with product managers to define hypotheses and design experiments aimed at improving the consumer experience.
    • Run A/B testing frameworks, quasi-experiments and statistical methodologies to measure the impact of product changes on key metrics such as user engagement, retention, and conversion rates.
    • Analyze experiment results to draw actionable insights and make data-driven recommendations for product iterations and enhancements.
  • Advance Analytics, Root Cause Analysis and Insights Generation:
    • Conduct in-depth analysis of user behavior data to uncover actionable insights and opportunities for product improvements.
    • Utilize advanced statistical techniques and machine learning algorithms to identify patterns, trends, and correlations in large datasets.
    • Work closely with cross-functional teams to translate insights into actionable recommendations for product enhancements and optimizations.
  • Collaboration with Product Managers:
    • Work hand-in-hand with product managers to understand user needs, pain points, and business objectives.
    • Provide analytical support and guidance to product teams throughout the product development lifecycle, from ideation to implementation.
    • Act as a strategic partner to product managers, helping them make informed decisions and prioritize features based on data-driven insights.
  • Developing Product and Feature Performance Tracking Dashboards:
    • Design and develop dashboards to track the performance of products and features, providing stakeholders with real-time visibility into key metrics and KPIs.
    • Implement data visualization best practices to ensure that dashboards are intuitive, informative, and actionable for product organizations.
    • Empower product managers and stakeholders to monitor the impact of product changes and make data-driven decisions to drive continuous improvement.
    • Highlight trends, anomalies, and areas for optimization through visually compelling dashboards, enabling proactive decision-making and rapid response to emerging issues.
The Day-to-Day Activities

  • Understand business objectives and deepdive into existing data to recommend new product ideas to solve for the most impactful customer problems
  • Use data to identify trends, spot anomalies and delve deeper into their root causes
  • Use your number-crunching and slide-maker skills to use and present data beyond just numbers. Hone your data storytelling skills through visualisation techniques by building dashboards and presenting insights to a non-technical audience!
  • Discuss and align with stakeholders on key product metrics, design and propose experimentation strategies
  • Launch A/B tests, analyze experiment results and provide recommendations
  • Design and own frontend and backend data specs for new products, while collaborating with engineering teams to ensure accurate and timely data collection
  • Develop and maintain data pipelines to fulfill product reporting requirements
  • Mentor junior team members, supporting their professional development in both hard and soft skills
  • Identify frequently occurring hypotheses and problem statements that can be generalised into common patterns. Develop scalable analytical frameworks and solutions to allow for solving repetitive problems in a more efficient manner
  • Role-model high standards of rigour in how we do measurements, experimentation, analyses and recommendations. Safeguard the integrity of how data is used and understood to ensure we maintain objectivity in our reasoning, actions and decision making
  • Evaluate the feasibility of developing new products, participate in user researches to validate customer problems


The Must-Haves

  • 1-3+ years of experience working in data-related and/or quantitative fields, including but not limited to Analytics and Applied Data Science. Fresh Graduates are open to apply too.
  • Fluent with SQL, Python, R or other scripting/programming languages to problem-solve. Experienced with working with very large datasets
  • Strong data visualization and storytelling skills. Experience in creating dashboards using Tableau or other visualization tools. Knowledge of Azure toolstack (Databricks, PowerBI, Azure Data Explorer) will be a plus
  • Effective communication and collaboration skills and the ability to present complex subjects coherently to diverse audiences comprising generalists and specialists
  • Self-motivator and an ability to learn independently
  • Ability to deliver on tight timelines and move quickly with cross-functional teams to partner or lead decision making while maintaining high attention to detail
  • A culture role model of our 4Hs: Honour, Humility, Hunger and Heart

The Nice-to-Haves

  • Relevant industry experience (preferably in an Internet or โ€˜Direct to Consumerโ€™ company with high velocity data and scaled controlled experiments) and/or prior experience with Product Management or business-facing operations will be a plus
  • Experience with designing and concluding controlled A/B experiments
  • Prior experience with supervised (eg : predictive modeling - regressions/classifications) and unsupervised (eg : clustering, segmentation) ML algorithms

Additional Information

We recognize that with these individual attributes come different workplace challenges, and we will work with Grabbers to address them in our journey towards creating inclusion at Grab for all Grabbers.

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