Senior Staff Applied Scientist, Science Platform

2d•$232,500 - $325,500•Remote

About The Position

Reddit’s Data Science Platform team builds shared tools and scientific capabilities that help data scientists and business partners understand Reddit’s communities and activity. The team develops Reddit’s analytics agent and methods for answering increasingly sophisticated questions. The Senior Staff Applied Scientist will serve as a senior technical lead, setting scientific direction for agentic analytics, causal inference, and forecasting. The role spans the full development cycle: identifying important problems, evaluating research, implementing and validating methods, and shipping reliable software that can be used across Reddit. The scientist will partner with software engineers, data scientists, and business partners to create an analytics agent that investigates questions, selects appropriate methods, executes analyses, and communicates findings with clear assumptions and uncertainty.

Requirements

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 behind their conclusions.
Experience developing and deploying forecasting systems, including time series modeling, temporal validation, prediction intervals, and assessing 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.
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 outputs.
Ability to communicate statistical assumptions, uncertainty, and technical tradeoffs clearly to business audiences.
Experience developing other technical contributors through mentorship, design reviews, and collaboration across organizational boundaries.
Ability to make progress on ambiguous problems, prioritize consequential work, and deliver useful capabilities through rapid iteration.

Nice To Haves

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.

Responsibilities

Define the scientific roadmap for the Data Science Platform across agentic analytics, causal inference, and forecasting, identifying methodological opportunities to improve decision-making at scale.
Research, evaluate, and implement emerging methods; prototype approaches, validate their practical value, and develop successful methods into maintained production capabilities.
Design and build the analytics agent’s architecture and capabilities, including question decomposition, scientific method selection, use of data and analytical tools, assumption checks, and reproducible results.
Build reusable causal inference tools to help teams estimate the effects of product changes, policies, and business decisions from observational data.
Develop shared forecasting capabilities for business and product metrics, including probabilistic predictions, hierarchical forecasts, and scenario analysis.
Create evaluation frameworks for the correctness and usefulness of agent-generated analyses, including benchmarks, simulations, and automated checks for methodological errors, unsupported claims, and misleading interpretations.
Own implementation of scientific methods in production software by 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 scientific recommendations, mentor scientists and engineers, and share methodological advances internally and externally where appropriate.

Benefits

Comprehensive healthcare benefits and income replacement programs.
401(k) with employer match.
Global benefit programs covering workspace, professional development, and caregiving support.
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; some positions may also be eligible for commission.
Medical, dental, and vision insurance for U.S.-based employees.