We’re looking for a talented Senior Principal Data Scientist to join Snap, Inc, reporting directly to the Director of Metrics & Insights. You will work directly in collaboration with senior leadership across Engineering to craft and drive the technical direction of our key metrics platforms. You will provide technical leadership and oversight for Snap’s Analytics Engineering, Decision Science, and Metrics Governance groups. You’ll be an indispensable part of a small but highly-leveraged team of engineers, data scientists, and researchers building out Snap’s core metric platforms.
What you’ll do:
Partner with Engineering, Product, and business partners in defining the technical direction of our metric platforms.
Partner with our research and data science teams to develop and implement methodologies in areas such as anomaly detection, causal inference, and user modeling.
Be a thought leader around how people visualize and understand data at Snap.
Collaborate with data scientists and business owners across the company to strategically define how we measure our business.
Actively contribute to and influence the roadmap for the Metrics & Insights organization.
Bachelor's in math, statistics, computer science, or other quantitative field or equivalent years of experience
Fluency in SQL or other big data querying language
Experience with at least one programming language (R, Python, Java, Scala); preference for Python
Experience with database tools such as BigQuery, Hadoop, Hive, and Spark
11+ years of experience in quantitative analysis & data science or a related field
MA/MS/PhD degree in math, statistics, computer science, or other quantitative field
Strong practical experience and theoretical understanding of machine learning methods with an understanding of limits and assumptions
Experience in both developing data-driven solutions and supporting them in production
Practical experience and theoretical understanding of A/B testing and platforms
Experience and theoretical understanding with causal inference and discovery at scale
Ability to gather insights from messy data and tell a story with effective communication
Ability to explain technical concepts and results of analyses to non-technical audiences
Experience in delivering results in a cross-functional environment
Experience with data pipeline monitoring and scheduling platforms (e.g., Airflow)
Experience with general analysis frameworks and tools such as scikit-learn, pandas, tensorflow, Tableau
Ability to initiate and drive projects to completion with minimal guidance
Being comfortable in a fast paced work environment
Ability to comprehend and debug complex systems that might cross team and tool boundaries across the company