Requirements:

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.

Nice to haves:

5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
2 years of work experience in data science or quantitative analytics with focus on statistical modeling, machine learning, and visualization.
Experience, education/training, or demonstrated interest in machine learning.
Excellent problem-solving skills and business judgment, articulating product questions, pulling data from datasets (SQL, BigQuery or equivalent technologies).
Excellent written and verbal communication skills and ability of translating analysis results into business recommendations.

Leverage advanced statistical methods on massive, datasets to extract insights from billions of events and thousands of features across organizational sources.
Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
Develop and deploy automated solutions, ranging from SQL query automation to real-time Python classification and ML modeling, to address key strategic challenges.
Analyze intricate product and platform usage patterns, translating data-driven insights into actionable product strategy and engineering decisions.
Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.