Senior Staff Applied Scientist, Science Platform

Details

Reddit's Data Science Platform team builds tools that enable scientific understanding of Reddit, including an analytics agent and shared scientific capabilities for data scientists and business partners. The role requires deep expertise in statistics, machine learning, and causal inference combined with strong software engineering skills.

Responsibilities

• Define the scientific roadmap for the Data Science Platform across agentic analytics, causal inference, and forecasting.
• Research, evaluate, and implement emerging methods, moving from research papers to prototypes and reliable production capabilities.
• Design and build the analytics agent's architecture and capabilities, including question decomposition, scientific method selection, data and analytical tool usage, assumption checking, and reproducible results.
• Build reusable causal inference tools to 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, benchmarks, simulations, and automated checks to measure the correctness and usefulness of agent-generated analyses and detect methodological errors, unsupported claims, and misleading interpretations.
• Implement scientific methods in production software using robust Python code, and partner 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 platform capabilities, documentation, and workflows.
• Influence technical and organizational strategy, mentor scientists and engineers, and share methodological advances internally and externally where appropriate.

Requirements

• 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.
• At least eight years of relevant industry experience 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, including practical experience developing causal methods for observational data and evaluating the assumptions supporting their conclusions.
• Experience developing and deploying forecasting systems, including time series modeling, temporal validation, prediction intervals, and performance assessment 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.
• Ability to critically assess scientific literature, reproduce relevant findings, and turn promising methods into reliable software.
• Experience developing LLM applications or analytical agents, including tool use, code execution, and systematic output evaluation.
• Ability to communicate statistical assumptions, uncertainty, and technical tradeoffs clearly to business audiences.
• Experience developing technical contributors through mentorship, design reviews, and cross-organizational collaboration.
• Ability to make progress on ambiguous problems, prioritize consequential work, and deliver useful capabilities through rapid iteration.

Preferred Qualifications

• Publications, open-source contributions, or widely adopted internal tools in agentic analytics, causal inference, forecasting, or 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 retrieving relevant data and context, selecting methods, executing Python or SQL, and validating findings.

Benefits

• 100% remote opportunity, with four office locations available for hybrid or onsite work preferences in New York, San Francisco, Los Angeles, and Chicago.
• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefits supporting workspace, professional development, and caregiving.
• Family planning support.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.
• Equity in the form of restricted stock units may be available, depending on the position offered.

Compensation

The base salary range is $232,500–$325,500 USD. The role may also be eligible for commission depending on the position offered. Final offer amounts depend on skills, depth of experience, and relevant licenses or credentials. Interviews may be recorded, transcribed, and summarized by artificial intelligence in select roles and locations, with an option to opt out before scheduled interviews.