Senior Staff Data Scientist – Consumer Relevance

Remote from: USA
Salary, yearly, USD: 232,500 - 325,500
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 2 Jun 2026
Apply before: 2 Jul 2026
Experience level: Senior
Views / Applies: 29 / 8

About company

About Reddit

Dive into anything

Internet
2005

Actively Hiring

AI Summary

Reddit is seeking a Senior Staff Data Scientist to lead relevance measurement and evaluation for its Consumer organization, focusing on feeds, search, and recommendations. The role involves developing metrics frameworks, designing experiments, and influencing product strategy in a complex, community-driven environment. The ideal candidate is a technical authority on ranking and recommendation systems, capable of tackling challenges like spillover effects and long-term user impact. They will partner with ML engineers and product teams to translate model performance into user-facing outcomes. This position offers an opportunity to shape how Reddit understands content quality and drives innovation across one of the internet's largest platforms.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight This role is extremely challenging due to the complexity of relevance in a networked platform, requiring deep expertise in ranking, experimentation, and causal inference, as well as the ability to influence product strategy and mentor others.

Salary Analysis

AI Insight The offered salary range of $232,500-$325,500 is competitive for a senior staff data scientist role, aligning with top-tier tech companies. The median of $279,000 is above the US market median for senior data scientists, reflecting the high level of responsibility and expertise required.

Key Skills

Data Science Machine Learning Recommendation Systems Experimentation Causal Inference A/B Testing Metrics Framework Ranking Search Product Strategy

Cover Letter Sample

Dear Hiring Manager,

I am excited to apply for the Senior Staff Data Scientist - Consumer Relevance position at Reddit. With over 10 years of experience in data science and a deep focus on recommendation systems and experimentation, I am drawn to Reddit's mission of bringing community and belonging to the world. I have a proven track record of developing metrics frameworks and designing experiments for ranking systems in complex, networked environments.

At my previous role at a major tech company, I led the evaluation of personalized feeds and search results, addressing challenges such as interference effects and long-term user engagement. I am skilled in causal inference, A/B testing, and translating model performance into product insights. I am eager to bring my expertise to Reddit's unique community-driven platform and help shape the relevance strategy.

Thank you for considering my application. I look forward to the opportunity to discuss how I can contribute to Reddit's growth and innovation.

Sincerely,
[Your Name]

Possible Interview Questions

How would you design an offline evaluation metric for a recommendation system that predicts long-term user retention?

I would start by identifying proxy metrics that correlate with retention, such as session length, diversity of content consumed, or user engagement with recommended items. Then, I would use historical data to validate these proxies through observational studies or causal inference methods. For offline evaluation, I would use a combination of ranking metrics like NDCG, MRR, and custom metrics that capture user satisfaction, such as click-through rates on recommendations. I would also incorporate counterfactual evaluation techniques to estimate the impact of ranking changes on retention.

Describe a time you had to account for spillover effects in an experiment on a networked platform. How did you handle it?

At a previous role, we experimented with a new ranking algorithm that could affect the supply of content from creators. We used a cluster-based randomization approach, grouping communities into clusters to minimize interference. We also monitored for spillover effects by analyzing metrics on control groups that were exposed to treated users. Additionally, we used causal inference methods like difference-in-differences to estimate the direct and indirect effects of the change.

How do you approach mentoring junior data scientists on experimentation best practices?

I believe in a hands-on approach: I pair with them on projects to guide them through the entire experimentation lifecycle, from hypothesis formulation to analysis and interpretation. I emphasize the importance of understanding the business context and potential biases. I also hold regular office hours and create documentation on common pitfalls, such as multiple testing corrections and power analysis. I encourage them to think critically about the assumptions behind their experiments and to validate results with observational data.

Can you explain a time you used causal inference to inform a product decision?

At my last company, we wanted to understand the impact of personalized recommendations on user retention. Since we couldn't run a long-term A/B test, I used instrumental variables, such as the time of day a user was active, to estimate the causal effect. I also employed propensity score matching to control for confounding factors. The analysis showed that personalized recommendations significantly improved retention, leading to a product investment in recommendation algorithms.

What metrics would you use to evaluate the quality of search results on Reddit?

I would use a combination of user engagement metrics like click-through rate, dwell time, and search abandonment rate. For relevance, I would use ranking metrics like NDCG at various cutoff levels, mean reciprocal rank, and precision@k. Additionally, I would consider user satisfaction surveys and content quality metrics such as the diversity of sources and freshness. To account for the community-driven nature, I would also measure the impact on community health, such as the number of new contributors or the proportion of high-quality content.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com.

Reddit is poised to rapidly innovate and grow like no other time in its history. This is a unique opportunity to leave your mark on one of the most influential and trafficked corners of the internet.

Consumer data science plays a key role in fulfilling Reddit’s mission of bringing community & belonging to the world through deep understanding of how we can better connect people to the best information and communities for them – the heart of Reddit’s product – from crypto to support groups, gaming to AMAs, travel tips to memes.

Reddit’s relevance challenges are uniquely complex. Our platform is a deeply interconnected network of communities, contributors, and consumers – where the notion of “relevance” spans personalized content ranking, community discovery, and search across an enormous corpus of authentic, user-generated content. We need a senior technical leader who thrives on these hard problems and can raise the bar for how we measure, evaluate, and improve the quality of recommendations and search results across the entire Consumer organization.

As a Senior Staff Data Scientist on the Consumer team, you will be the go-to expert on relevance measurement and evaluation, partnering closely with Feeds and Search ML teams to tackle the most complex ranking, recommendation, and retrieval challenges across Consumer. You will shape how Reddit understands content quality, define the metrics and analytical frameworks that guide relevance improvements, and influence product strategy through rigorous analysis and experimentation.

Responsibilities

Serve as the technical authority on relevance metrics and evaluation methodology across Consumer, setting standards for how we measure the quality of feeds, search results, and recommendations in a complex, community-driven environment
Develop metrics frameworks and offline evaluation approaches for ranking and recommendation systems, including proxy metrics that reliably predict long-term outcomes like retention, community health, and user satisfaction
Design and analyze experiments for relevance features, accounting for challenges unique to networked platforms such as spillover effects between communities, interference between contributors and consumers, and long-run impacts of ranking changes on content supply
Identify opportunities where improved measurement and analysis can unlock product insights that were previously unmeasurable or ambiguous, particularly around content quality, search intent understanding, and personalization effectiveness
Partner deeply with ML engineers and product teams to translate model performance metrics into user-facing impact
Influence the long-term product strategy for Feeds and Search by synthesizing insights from experimentation, observational analysis, and metric deep-dives into clear, actionable recommendations for senior leadership
Mentor and elevate other data scientists across the organization on relevance evaluation, experimentation best practices for ranking systems, causal reasoning, and statistical rigor
Publish and share methodological advances internally and, where appropriate, externally to contribute to the broader relevance, recommendation systems, and experimentation community

Required Qualifications

Ph.D. in Statistics, Computer Science, Information Retrieval, Economics, or a related quantitative field with a strong focus on recommendation systems, ranking, causal inference, or evaluation methodology; or M.S. with equivalent depth of expertise
For M.S. holders: 12+ years of industry experience in applied science, data science, or relevance/ranking-focused roles
For Ph.D. holders: 8+ years of industry experience in applied science, data science, or relevance/ranking-focused roles
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 practical experience with challenges relevant to ranking systems such as novelty effects, position bias, long-run effect estimation, and ecosystem-level impacts
Experience defining and validating quality metrics for content ranking, search, or recommendations at scale
Strong theoretical grounding in experimental design, including power analysis, variance reduction techniques, and sequential testing as applied to relevance experiments
Expert knowledge of SQL and proficiency in R and/or Python for statistical computing
Demonstrated ability to influence product and organizational strategy through data-driven insights about content quality and user experience
Excellent communication skills with the ability to explain nuanced statistical and ML concepts and tradeoffs to both technical and non-technical senior stakeholders
Experience mentoring data scientists and building organizational capability in relevance evaluation and experimentation
Comfortable in innovative and fast-paced environments with a bias toward action

Preferred Qualifications

Published research or industry contributions in areas recommendation systems or causal inference for ranking
Experience with social network or user-generated content platforms where community-level dynamics create non-trivial relevance and experimentation challenges

Benefits:

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

#LI-REMOTE

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 learn more, please visit https://www.redditinc.com/careers/.

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

In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

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