The job, in the employer's words
Reddit is continuing to grow our teams with the best talent. This role is completely remote friendly within the United States. If you happen to live close to one of our physical office locations (San Francisco, Los Angeles, New York City & Chicago) our doors are open for you to come into the office as often as you'd like.
Reddit is poised to innovate and grow like never before, and Safety is a critical accelerant of that growth. The Safety org is Reddit’s central Trust & Safety organization, protecting users from bad experiences by stopping harmful content, behaviors, and abuse across the platform. We are looking for a Senior Data Scientist to lead ads fraud detection and scaled enforcement within Safety. You will partner closely with Ads Product, Engineering, Machine Learning, Operations, Policy, Legal, and fellow Safety data scientists to identify emerging ads fraud, define rigorous measurement and evaluation standards, and turn investigations into durable signals, models, rules, and enforcement pipelines. This is a high-impact role with exceptional opportunity for ownership and growth: as an early leader in a greenfield space, you will help define the strategy, shape cross-functional roadmaps, build foundational capabilities, and expand your scope as Reddit’s ads integrity program matures.
Responsibilities
- Lead the measurement and detection strategy for ads fraud by defining fraud taxonomies, labels, sampling plans, metrics, and evaluation frameworks that make performance measurable and defensible.
- Analyze large, complex datasets and networks of behavior to uncover emerging fraud patterns, size their impact, identify root causes, and translate findings into detection and enforcement requirements.
- Design and develop scalable ads fraud detection and enforcement pipelines in partnership with Engineering and Machine Learning, including feature generation, rules and models, near-real-time scoring, actioning, review feedback loops, and observability.
- Own the full detection lifecycle: backtesting, threshold calibration, offline and online evaluation, launch validation, experimentation, monitoring, drift detection, incident response, rollback, and retirement.
- Build and maintain statistical, machine learning, and GenAI-enabled models or prototypes that improve fraud detection, risk identification, investigator efficiency, and enforcement quality.
- Balance fraud loss, platform and advertiser risk, customer experience, false-positive costs, operational capacity, and business goals when recommending detection thresholds and enforcement strategies.
- Partner across Ads and Safety to shape strategy and roadmaps, strengthen data foundations, close policy and enforcement gaps, and ensure solutions meet governance and compliance standards.
- Translate complex analyses into clear narratives and actionable recommendations for technical and non-technical stakeholders, including senior leaders, and mentor other data scientists and analysts.
Qualifications
- Relevant experience in Data Science, Applied Science, or a related quantitative role, preferably in ads fraud, financial fraud, or account risk, Trust & Safety, platform integrity, or enforcement engineering.
- Ph.D. or M.S. degree in Statistics, Economics, Computer Science, Applied Mathematics, or another quantitative field; with an M.S., 4+ years of industry data science experience, or with a Ph.D., 2+ years of industry data science experience.
- Demonstrated experience building or materially shaping production detection and automated enforcement pipelines, including batch or streaming data, feature engineering, rules or models, decisioning, monitoring, and feedback loops.
- Strong command of fraud or abuse detection methods and evaluation, including label design, precision and recall tradeoffs, calibration, threshold selection, false-positive analysis, drift detection, and adversarial adaptation.
- Experience partnering closely with Product and Engineering teams to translate analyses and prototypes into reliable production systems; experience working across Ads, Safety, fraud, risk, or platform-integrity organizations is preferred.
- Experience applying AI and large language models (LLMs) to practical data science workflows, such as threat discovery, content classification, signal development, investigation automation, or detection and enforcement systems.
- Deep understanding of complex behavioral networks or large-scale activity patterns; experience with methods such as graph or network analysis, clustering, anomaly detection, or natural language processing is valuable.
- Fluency in statistical analysis, Python or a similar programming language, and SQL, with the ability to work independently across complex data systems and unfamiliar codebases.
- Ability to tackle ambiguously defined problems, deconstruct them into precise and tractable components, and move from investigation to scalable, reusable solutions.
- Strong technical leadership and communication skills, with a track record of influencing cross-functional roadmaps, aligning stakeholders, and explaining complex topics to technical and non-technical audiences.
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-SP1
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.
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.
Benefits mentioned
- 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-SP1
Requirements mentioned
- Relevant experience in Data Science, Applied Science, or a related quantitative role, preferably in ads fraud, financial fraud, or account risk, Trust & Safety, platform integrity, or enforcement engineering.
- Ph.D. or M.S. degree in Statistics, Economics, Computer Science, Applied Mathematics, or another quantitative field; with an M.S., 4+ years of industry data science experience, or with a Ph.D., 2+ years of industry data science experience.
- Demonstrated experience building or materially shaping production detection and automated enforcement pipelines, including batch or streaming data, feature engineering, rules or models, decisioning, monitoring, and feedback loops.
- Strong command of fraud or abuse detection methods and evaluation, including label design, precision and recall tradeoffs, calibration, threshold selection, false-positive analysis, drift detection, and adversarial adaptation.
- Experience partnering closely with Product and Engineering teams to translate analyses and prototypes into reliable production systems; experience working across Ads, Safety, fraud, risk, or platform-integrity organizations is preferred.
- Experience applying AI and large language models (LLMs) to practical data science workflows, such as threat discovery, content classification, signal development, investigation automation, or detection and enforcement systems.
- Deep understanding of complex behavioral networks or large-scale activity patterns; experience with methods such as graph or network analysis, clustering, anomaly detection, or natural language processing is valuable.
- Fluency in statistical analysis, Python or a similar programming language, and SQL, with the ability to work independently across complex data systems and unfamiliar codebases.
- Ability to tackle ambiguously defined problems, deconstruct them into precise and tractable components, and move from investigation to scalable, reusable solutions.
- Strong technical leadership and communication skills, with a track record of influencing cross-functional roadmaps, aligning stakeholders, and explaining complex topics to technical and non-technical audiences.
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