Senior Staff Applied Scientist, Science Platform

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.

Required Qualifications

• 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 Qualifications

• 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.

Benefits

• 100% remote opportunity (we have four office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)
• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401K with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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Pay Transparency

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is: $232,500 - $325,500 USD

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