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Job Description & How to Apply Below
Responsibilities
- 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 a deep familiarity with the source data and its generating systems through documentation.
- Interacting with the engineering teams and systematic data profiling.
- Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus.
- 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 deep 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.
- Excellent SQL expertise.
- Competence with reproducible data analysis using Python or R.
- Familiarity with data modeling and dimensional design.
- Experience designing and analyzing experiments using A/B testing, multivariate testing, switchback experiments, and synthetic control methods.
- 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 including descriptive, exploratory, inferential, causal, and predictive analysis.
- Deep understanding of various experiment design and analysis workflows and the corresponding statistical techniques.
- Familiarity with product data (impressions, events, etc.) and product health measurement (conversion, engagement, retention, etc.).
- Familiarity with Big Query and the Google Cloud Platform (plus).
- Data engineering and data pipeline development experience (e.g., via Airflow) (plus).
- Experience with classical ML frameworks (e.g., Scikit-learn, XGBoost, Light
GBM) (plus). - Bachelor's degree in engineering, computer science, technology, or similar fields.
- Postgraduate degree is a plus but not required.
- 5+ years of overall experience working in data science and machine learning.
- Experience doing data science in an online consumer product setting (plus).
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