below marketmost similar roles pay hereabove market
This role pays more than
94%
of similar roles. Most pay
$162,000–$232,850
— the blue band above.
At the midpoint, this role pays about
$279k
versus about
$197k
for comparable roles.
Based on 240 similar postings.
Employer
About Reddit
Reddit is a social news aggregation and discussion platform where users share content, vote on posts, and engage in community conversations across thousands of interest-based forums called subreddits.
Reddit currently has
89 open roles
on FindRole.
Listed pay typically runs
$216,700–$303,000
across 89 roles with salary data.
JOB TITLE: Senior Staff Data Scientist, Consumer Relevance
As a Senior Staff Data Scientist on the Consumer team, you will serve as the technical authority on relevance measurement and evaluation for feeds, search, and recommendations. You will partner with Feeds and Search ML teams to develop metrics frameworks, offline evaluation approaches, and proxy metrics for long-term outcomes like retention and community health. Your work involves designing experiments to account for networked platform challenges like spillover effects and position bias. You will use SQL, R, and Python to provide causal inference and statistical rigor to ranking systems. The role addresses the complex problem of measuring content quality and personalization effectiveness across a massive corpus of authentic, user-generated content within a deeply interconnected network of communities and contributors.
What does a Data Scientist earn in Remote?
Median $216300 from 36 postings across 16 companies.
Serve as the technical authority on relevance metrics and evaluation methodology across the Consumer organization.
Develop metrics frameworks and offline evaluation approaches for ranking and recommendation systems.
Design and analyze experiments for relevance features while accounting for networked platform challenges.
Identify opportunities to unlock product insights regarding content quality, search intent, and personalization.
Partner with ML engineers and product teams to translate model performance into user-facing impact.
Influence long-term product strategy by synthesizing insights from experimentation and metric deep-dives.
Mentor other data scientists on relevance evaluation, causal reasoning, and statistical rigor.
Publish methodological advances internally and externally to contribute to the broader recommendation systems community.
What we're looking for
Ph.D. in Statistics, Computer Science, Information Retrieval, Economics, or a related quantitative field with a focus on recommendation systems, ranking, causal inference, or evaluation methodology.
M.S. in a related quantitative field with equivalent depth of expertise to a Ph.D.
12+ years of industry experience in applied science, data science, or relevance/ranking-focused roles for M.S. holders.
8+ years of industry experience in applied science, data science, or relevance/ranking-focused roles for Ph.D. holders.
Deep expertise in metrics design and evaluation for ranking and recommendation systems, including offline metrics and counterfactual evaluation.
Strong understanding of causal inference and experimentation methodology, including novelty effects, position bias, and long-run effect estimation.
Experience defining and validating quality metrics for content ranking, search, or recommendations at scale.
Expert knowledge of SQL and proficiency in R and/or Python for statistical computing.
Strong theoretical grounding in experimental design, including power analysis, variance reduction techniques, and sequential testing.
Demonstrated ability to influence product and organizational strategy through data-driven insights about content quality and user experience.
Excellent communication skills to explain nuanced statistical and ML concepts to both technical and non-technical senior stakeholders.
Experience mentoring data scientists and building organizational capability in relevance evaluation and experimentation.
Published research or industry contributions in areas of recommendation systems or causal inference for ranking (preferred).
Experience with social network or user-generated content platforms where community-level dynamics create non-trivial relevance and experimentation challenges (preferred).
Machine Learning
Deep Learning
Generative AI
LLM
Python
SQL
BigQuery
Hugging Face
Google Vertex AI
AWS
Transformers
Embeddings
Recommendation Systems
Neural Ranking
Information Retrieval
Claude Code