Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
Communication @ 7
Compliance
Data Science @ 4
Experimentation
Fraud @ 4
GenAI
LLM
Leadership @ 7
Machine Learning
Mathematics
NLP
Observability
Python @ 6
SQL @ 6
Statistics
Technical Leadership @ 7
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Reddit is seeking a Senior Data Scientist to lead ads fraud detection and scaled enforcement within the Safety organization. This role partners with Ads Product, Engineering, Machine Learning, Operations, Policy, Legal, and Safety data scientists to identify emerging ads fraud, establish measurement and evaluation standards, and turn investigations into durable signals, models, rules, and enforcement pipelines. As an early leader in this area, the role will help define strategy, shape cross-functional roadmaps, build foundational capabilities, and expand Reddit’s ads integrity program.
Responsibilities
- Lead the measurement and detection strategy for ads fraud by defining fraud taxonomies, labels, sampling plans, metrics, and evaluation frameworks.
- Analyze large, complex datasets and behavioral networks to uncover emerging fraud patterns, measure their impact, identify root causes, and translate findings into detection and enforcement requirements.
- Design and develop scalable ads fraud detection and enforcement pipelines 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, including 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.
- Mentor other data scientists and analysts.
Requirements
- Relevant experience in Data Science, Applied Science, or a related quantitative role, preferably in ads fraud, financial fraud, 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., at least 4 years of industry data science experience; with a Ph.D., at least 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 with Product and Engineering teams to translate analyses and prototypes into reliable production systems. Experience across Ads, Safety, fraud, risk, or platform-integrity organizations is preferred.
- Experience applying AI and large language models 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 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 ambiguous 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.
- 401(k) with employer match.
- Global benefit programs covering workspace, professional development, caregiving support, and other needs.
- Family planning support.
- Gender-affirming care.
- Mental health and coaching benefits.
- Flexible vacation and paid volunteer time off.
- Generous paid parental leave.
- Equity in the form of restricted stock units.
Work Arrangement
The role is completely remote friendly within the United States. Employees near Reddit offices in San Francisco, Los Angeles, New York City, or Chicago may use those offices as often as they wish.
Compensation
The base salary range is $190,800–$267,100 USD. The position may also be eligible for equity and, depending on the position offered, commission.