About the job
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.
Building deep partnerships with product and business counterparts.
Setting the team's roadmap, success criteria, and priorities in close collaboration with engineering and business partners.
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.
Maintain a balance between building sustainable practices and going after high-impact projects on one hand and identifying low-cost, high-impact wins and intelligent scoping to deliver value sooner on the other.
Coach and mentor our data scientists and data analysts technically and professionally.
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.
Requirements
-
Technical Experience
- 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.
- Competence with reproducible data analysis using Python or R and excellent SQL skills.
-
Comfortable with data modeling and dimensional design.
-
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.
-
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.
- 5+ years of overall experience working in data science.
- 2+ years of overall experience in leading data teams.
- 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 and communicator.
- A strong sense of ownership and accountability.
- A ‘keep it simple’ approach to make it happen.
