Reddit brings people together around their interests, questions, and experiences. Our communities generate a rich and complex ecosystem of human activity, creating scientific challenges in understanding behavior, measuring impact, and anticipating change.

The Data Science Platform team builds the tools that enable a scientific understanding of Reddit. We develop Reddit’s analytics agent, and the shared scientific capabilities and methods that allow data scientists and business partners to answer increasingly sophisticated questions.

We are looking for a senior technical lead who combines deep expertise in statistics, machine learning and causal inference with strong software engineering ability. You will establish the scientific direction for agentic analytics, causal inference, and forecasting, turning advances in research into reliable software used across the company.

As an Applied Scientist on the Science Platform team, you will work across the full development cycle: identifying important problems, evaluating research, implementing and validating methods, and shipping capabilities that others can use confidently. You will partner closely with software engineers and data scientists to build an analytics agent that can investigate questions, select appropriate methods, execute analyses, and communicate findings with clear assumptions and uncertainty.

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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