Senior Staff Applied Scientist, Science Platform
New
R
Reddit, Inc.Data science
Remote - United StatesFull-TimeStaff
Salary$232,500 — $325,500 USD
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Job Details
- Experience
- At least eight years of relevant industry work following a doctorate, or ten years with a master’s degree
- Required Skills
- PythonSQL
Requirements
- Hold a doctorate in Statistics, Economics, Computer Science, Applied Mathematics, Operations Research, or a related discipline, or a master’s degree supported by comparable depth of expertise.
- Have at least eight years of relevant industry work after a doctorate, or ten years with a master’s degree.
- Bring deep knowledge of statistical inference and econometrics.
- Have practical experience developing causal methods for observational data and evaluating the assumptions behind their conclusions.
- Have experience developing and deploying forecasting systems, including time series modeling, temporal validation, prediction intervals, and performance assessment as conditions change.
- Have substantial experience writing and maintaining production Python software.
- Be familiar with API design, automated testing, code review, version control, and operational monitoring.
- Demonstrate the ability to assess scientific literature, reproduce relevant findings, and turn methods into reliable software.
- Have experience developing LLM applications or analytical agents, including tool use, code execution, and systematic evaluation.
- Communicate statistical assumptions, uncertainty, and technical tradeoffs to business audiences.
- Develop other technical contributors through mentorship, design reviews, and cross-organizational collaboration.
- Make progress on ambiguous problems, prioritize consequential work, and deliver useful capabilities through rapid iteration.
Responsibilities
- Define the scientific roadmap for Data Science Platform across agentic analytics, causal inference, and forecasting.
- Research, evaluate, and implement emerging methods, validating prototypes and developing successful approaches into maintained production capabilities.
- Design and build analytics agent architecture and capabilities for decomposing questions, selecting methods, using tools, checking assumptions, and producing reproducible results.
- Build reusable causal inference tools to estimate effects of product changes, policies, and business decisions using observational data.
- Develop shared forecasting capabilities for business and product metrics, including probabilistic predictions, hierarchical forecasts, and scenario analysis.
- Create evaluation frameworks, benchmarks, simulations, and automated checks for agent-generated analyses.
- Implement scientific methods in production software using robust Python and collaborate on libraries, APIs, data pipelines, observability, and system design.
- Translate recurring analytical needs into platform capabilities, documentation, and workflows with data scientists and business partners.
- Make scientific recommendations, mentor scientists and engineers, and share methodological advances.
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