Reddit Senior Staff Applied Scientist at Reddit's Data Science Platform team leading scientific direction for agentic analytics, causal inference, and forecasting. Responsible for research, implementation, and production of scientific methods.
Responsibilities
Define the scientific roadmap for Data Science Platform across agentic analytics, causal inference and forecasting, identifying opportunities where advances in methodology can improve decision-making at scale across Reddit.
Research, evaluate, and implement emerging methods. Move quickly from research papers to working prototypes, validate their practical value, and develop successful approaches into maintained production capabilities.
Design and build our analytics agent’s architecture and capabilities, including how the agent decomposes questions, selects scientific methods, uses data and analytical tools, checks assumptions, and produces reproducible results.
Build reusable causal inference tools that help teams estimate the 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 that measure the correctness and usefulness of agent-generated analyses. Build benchmarks, simulations, and automated checks that detect methodological errors, unsupported claims, and misleading interpretations.
Own the implementation of scientific methods in production software, writing robust Python code and partnering with engineers on libraries, APIs, data pipelines, observability, and system design.
Work with data scientists and business partners to identify recurring analytical needs and translate them into accessible platform capabilities, documentation, and workflows.
Influence technical and organizational strategy through clear scientific recommendations, mentor scientists and engineers, and share methodological advances internally and externally where appropriate.
Qualification
A doctorate in StatisticsStrong software engineering skillsPublications
Required
A doctorate in Statistics, Economics, Computer Science, Applied Mathematics, Operations Research, or a related discipline; alternatively, a master’s degree supported by comparable depth of expertise.
At least eight years of relevant industry work following a doctorate, or ten years with a master’s degree, in applied science, data science, scientific software development, or related roles.
Deep knowledge of statistical inference and econometrics, with practical experience developing causal methods for observational data and evaluating the assumptions that make their conclusions credible.
Experience developing and deploying forecasting systems, including time series modeling, temporal validation, prediction intervals, and assessment of performance as underlying conditions change.
Strong software engineering skills, including substantial experience writing and maintaining production Python software. Familiarity with API design, automated testing, code review, version control, and operational monitoring.
A demonstrated ability to critically assess scientific literature, reproduce relevant findings, and turn promising methods into reliable software that solves practical problems.
Experience developing LLM applications or analytical agents, including tool use, code execution, and systematic evaluation of their outputs.
Clear communication of statistical assumptions, uncertainty, and technical tradeoffs to business audiences.
Experience developing other technical contributors through mentorship, design reviews, and collaboration across organizational boundaries.
The ability to make progress on ambiguous problems, prioritize consequential work, and deliver useful capabilities through rapid iteration.
Preferred
Publications, open-source contributions, or widely adopted internal tools in agentic analytics, causal inference, forecasting, or other related fields.
Experience building scientific libraries or platforms used by other data scientists, including interfaces that make sophisticated methods accessible to people with different levels of statistical expertise.
Experience building agents at scale that perform substantive analytical work, including retrieval of relevant data and context, selection of methods, execution of Python or SQL, and validation of findings.
