Senior Data Scientist, Ads Integrity
New
R
Reddit, Inc.Trust & Safety
Remote - United StatesFull-TimeSenior
Salary$190,800 — $267,100 USD
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Job Details
- Experience
- M.S. with 4+ years of industry data science experience, or Ph.D. with 2+ years of industry data science experience.
- Required Skills
- PythonSQLMachine LearningData scienceNLPLLM
Requirements
- Relevant experience in Data Science, Applied Science, or a related quantitative role (preferably in ads fraud, financial fraud, account risk, Trust & Safety, or platform integrity).
- Ph.D. or M.S. degree in Statistics, Economics, Computer Science, Applied Mathematics, or another quantitative field.
- 4+ years of industry experience with an M.S., or 2+ years with a Ph.D.
- Demonstrated experience building or shaping production detection and automated enforcement pipelines (batch or streaming data, feature engineering, rules/models, decisioning, feedback loops).
- Strong command of fraud/abuse detection and evaluation (label design, precision/recall, calibration, false-positive analysis, drift detection, adversarial adaptation).
- Experience partnering closely with Product and Engineering to translate analyses and prototypes into reliable production systems.
- Experience applying AI and large language models (LLMs) to practical data science workflows.
- Deep understanding of complex behavioral networks or large-scale activity patterns (graph/network analysis, clustering, anomaly detection, or NLP).
- Fluency in statistical analysis, Python (or similar), and SQL.
- Ability to tackle ambiguously defined problems and move from investigation to scalable solutions.
- Strong technical leadership and communication skills, with a track record of influencing cross-functional roadmaps.
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 networks of behavior to uncover emerging fraud patterns, size their impact, identify root causes, and translate findings into requirements.
- Design and develop scalable ads fraud detection and enforcement pipelines in partnership with Engineering and Machine Learning.
- Own the full detection lifecycle, including backtesting, threshold calibration, evaluation, launch validation, experimentation, monitoring, and incident response.
- Build and maintain statistical, machine learning, and GenAI-enabled models or prototypes that improve fraud detection and investigator efficiency.
- Balance fraud loss, platform risk, customer experience, false-positive costs, and business goals when recommending strategies.
- Partner across Ads and Safety to shape strategy, strengthen data foundations, and ensure solutions meet governance and compliance standards.
- Translate complex analyses into clear narratives for technical and non-technical stakeholders and mentor other data scientists.
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