Staff Data Scientist - Consumer
New
US remote-friendly or any office location - SF, LA, CHI, NYFull-TimeStaff
Salary217,000 - 303,900 USD per year
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Job Details
- Experience
- For M.S. holders: 10+ years of industry experience in applied science or data science roles For Ph.D. holders: 6+ years of industry experience in applied science or data science roles
- Required Skills
- PythonSQLR
Requirements
- Advanced degree (Master's or Ph.D.) in Statistics, Mathematics, Physics, Economics, or Operations Research
- For M.S. holders: 10+ years of industry experience in applied science or data science roles
- For Ph.D. holders: 6+ years of industry experience in applied science or data science roles
- Expert knowledge of SQL and relational databases
- Familiarity with statistical analysis and the preferred programming languages of the team (R / Python)
- Demonstrated ability to influence and guide product strategy with data
- Demonstrated ability to take ambiguous problems and solve them in a structured, hypothesis-driven, data-supported way
- Familiarity with causal inference techniques and causal models
- Excellent communication and experience working with senior leaders / stakeholders
- Entrepreneurial, self-directed, and enthusiastic about solving problems
- Demonstrated ability to mentor other data scientists and share best practices to elevate the data science practice at Reddit
- Conform in innovative and fast-paced environments, and have a bias toward action.
- Can discuss complex topics with technical and non-technical audiences
Responsibilities
- Help build the long term product strategy by identifying opportunities to attract new users, increase engagement, and drive retention
- Influence strategic roadmaps through data-driven insights into user behaviors and needs
- Design metrics that help evaluate the health of the business and the success of our products, including any ETL development needed for consistent and robust analysis
- Drive experimentation from design through execution and analysis to maximize learnings and guide future investment decisions
- Build self-serve tools for product and engineering partners that answer common questions and/or increase data literacy in the organization
- Work cross functionally with product, engineering, and design teams to ensure insights make it to the product
- Scale your work to other parts of the data organization through mentoring more junior data scientists, improving processes, and providing perspective on the most important problems
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