Senior Staff Applied Scientist, Science Platform

Description

The company is a community of communities, built on shared interests and trust. Users submit, vote and comment every day on the topics they care about most.

The platform has 100,000+ active communities and approximately 130 million daily active unique visitors, making it one of the largest sources of information on the internet.

The communities form a complex ecosystem of human activity, which raises scientific questions about behavior, measuring impact and anticipating change.

The Data Science Platform team builds the tools for that scientific understanding. It develops the analytics agent and shared methods that let data scientists and business partners answer harder questions.

The team is looking for a senior technical lead with deep statistics, machine learning and causal inference skills, plus strong software engineering. This person will set the scientific direction for agentic analytics, causal inference and forecasting, and turn research into reliable software used across the company.

As an Applied Scientist, you will cover the full cycle: spotting important problems, assessing research, building and validating methods, and shipping work people can trust. You will work closely with engineers and data scientists on an analytics agent that investigates questions, picks methods, runs analyses and reports findings with stated assumptions and uncertainty.

Responsibilities

• Set the scientific roadmap for the platform across agentic analytics, causal inference and forecasting, finding where better methods can improve decisions at scale.
• Assess new methods quickly: move from paper to prototype, test real-world value, and turn successful approaches into maintained production capabilities.
• Architect the analytics agent, covering how it breaks down questions, chooses methods, uses data and tools, checks assumptions and produces reproducible output.
• Create reusable causal inference tools that estimate the effects of product changes, policies and business decisions from observational data.
• Build shared forecasting capabilities for business and product metrics, including probabilistic and hierarchical forecasts and scenario analysis.
• Build evaluation frameworks for agent-generated analyses, using benchmarks, simulations and automated checks to catch methodological errors, unsupported claims and misleading interpretations.
• Own production implementations of scientific methods in Python, working with engineers on libraries, APIs, data pipelines, observability and system design.
• Work with data scientists and business partners to find recurring analysis needs and turn them into platform features, documentation and workflows that are easy to use.
• Shape technical and organizational strategy through scientific recommendations, mentor scientists and engineers, and share methods internally and externally where suitable.

Requirements

• A doctorate in Statistics, Economics, Computer Science, Applied Mathematics, Operations Research or a related discipline, or a master's degree backed by comparable depth of expertise.
• Eight years of relevant industry work after a doctorate, or ten years after a master's, in applied science, data science, scientific software development or similar roles.
• Deep knowledge of statistical inference and econometrics, with practical causal methods for observational data and the judgment to assess whether their assumptions hold.
• Experience building and deploying forecasting systems, including time series modeling, temporal validation, prediction intervals and tracking performance as conditions change.
• Strong software engineering, including substantial production Python, API design, automated testing, code review, version control and operational monitoring.
• Ability to critically read scientific literature, reproduce findings and turn promising methods into reliable software.
• Experience with LLM applications or analytical agents, including tool use, code execution and systematic evaluation of outputs.
• Ability to explain statistical assumptions, uncertainty and technical tradeoffs clearly to business audiences.
• Experience mentoring other technical contributors, leading design reviews and working across organizational boundaries.
• Ability to make progress on ambiguous problems, prioritize high-impact 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.
• Preferred qualifications: experience building scientific libraries or platforms used by data scientists, including interfaces that make advanced methods usable at different levels of statistical expertise.
• Preferred qualifications: experience building agents at scale that do substantive analytical work, including retrieving data and context, selecting methods, executing Python or SQL and validating findings.

Conditions

This posting may span more than one career level.

The base salary range is $232,500 - $325,500 USD. Final offers depend on skills, depth of work experience and relevant licenses or credentials, and may fall outside this range.

Base salary ranges are shared for all US-based postings regardless of state. Standard ranges are set by function, level and country, benchmarked against similar growth-stage companies.

Beyond base salary, the role is eligible for restricted stock units and, depending on the position, may also be eligible for commission.

US-based employees receive medical, dental and vision insurance, a 401(k) with employer match, generous vacation time and parental leave.

This is a 100% remote opportunity, with hybrid or onsite work available at four offices in NY, SF, LA and Chicago.

In select roles and locations, interviews may be recorded, transcribed and summarized by artificial intelligence, and you can opt out before any scheduled interview.

The company is an equal opportunity employer that provides reasonable accommodations to qualified individuals with disabilities and disabled veterans, so tell your recruiter if you need one during interviews.

Benefits

• Comprehensive healthcare 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.