As the leading delivery company in the region, we have a significant responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users. As a data scientist on the analysis track, your mission will be to improve the quality of decisions made across product and business via relevant, reliable, and actionable data. You will own a particular domain across product and business and work closely with corresponding product and business managers as part of a talented team of data scientists and data engineers. You will manage the entire data value chain including logging, data modeling, analysis, reporting, and experimentation.
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What’s On Your Plate?
- Leveraging ambiguous business problems as opportunities to drive objective criteria using data.
- Developing a deep understanding of the product experiences and business processes that make up your area of focus.
- Developing familiarity with the source data and its generating systems through documentation, interaction with engineering teams, and systematic data profiling.
- Contributing to the design and maintenance of the data models that measure performance and comprehend performance drivers.
- Working closely with product and business teams to identify important questions that can be answered effectively with data.
- Delivering well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision making through analysis and automated reports.
- Designing, planning, and analyzing experiments (A/B and multivariate tests).
- Supporting product and business managers with KPI design and goal setting.
- Mentoring other data scientists in their growth journeys.
- Contributing to improving our ways of work, our tooling, and our internal training programs.
What Did We Order?
- Excellent SQL.
- Competence with reproducible data analysis using Python or R.
- Familiarity with data modeling and dimensional design.
- Strong command over the entire data analysis lifecycle including problem formulation, data auditing, rigorous analysis, interpretation, recommendations, and presentation.
- Familiarity with different types of analysis: descriptive, exploratory, inferential, causal, and predictive analysis.
- Deep understanding of experiment design and analysis workflows and statistical techniques.
- Familiarity with product data (impressions, events) and product health measurement (conversion, engagement, retention).
- Familiarity with BigQuery and Google Cloud Platform is a plus.
- Data engineering and pipeline development experience (e.g., via Airflow) is a plus.
- Experience with classical ML frameworks (e.g., Scikit-learn, XGBoost, LightGBM) is a plus.
What You Need to Be Successful
- Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.
- 3+ years of overall experience in data science and machine learning.
- Experience in data science in an online consumer product setting is a plus.
- A good problem solver with a growth mindset.
- An excellent collaborator and communicator.
- A strong sense of ownership and accountability.
- A ‘keep it simple’ approach to make it happen.
