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 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.
- Mentoring other data scientists in their growth journeys.
- Contributing to improving our ways of work, our tooling, and our internal training programs.
What you need to be successful
What Did We Order?
Technical Experience
- 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 including; descriptive, exploratory, inferential, causal, and predictive analysis.
- Deep understanding of the various experiment design and analysis workflows and the corresponding statistical techniques.
- Familiarity with product data (impressions, events, ..) and product health measurement (conversion, engagement, retention, ..).
- Familiarity with BigQuery and the Google Cloud Platform is a plus.
- Data engineering and data pipeline development experience (e.g. via Airflow) is a plus.
- Experience with classical ML frameworks (e.g. Scikit-learn, XGBoost, LightGBM, ...) is a plus.
Qualifications
- Bachelor's degree in engineering, computer science, technology, or similar fields. A 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 is a plus.
- A good problem solver with a ‘figure it out’ growth mindset.
- An excellent collaborator.
- An excellent communicator.
- A strong sense of ownership and accountability.
- A ‘keep it simple’ approach to #makeithappen.