Senior Data Scientist, Ads Integrity
Leads ads fraud measurement, detection, and automated enforcement for Reddit Safety, translating behavioral analysis into production pipelines, models, rules, and governance. Requires advanced quantitative education, substantial data science experience, and expertise in fraud evaluation, machine learning, Python, and SQL.
About the job
Responsibilities
- Lead the measurement and detection strategy for ads fraud, including fraud taxonomies, labels, sampling plans, metrics, and evaluation frameworks.
- Analyze large, complex datasets and behavioral networks to uncover fraud patterns, quantify 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, feedback loops, and observability.
- Own the 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 for 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 thresholds and enforcement strategies.
- Partner across Ads and Safety to shape strategy and roadmaps, improve data foundations, close policy and enforcement gaps, and meet governance and compliance standards.
- Communicate complex analyses and recommendations to technical and non-technical stakeholders, including senior leaders, and mentor data scientists and analysts.
Requirements
- Experience in Data Science, Applied Science, or a related quantitative role; experience in ads fraud, financial fraud, account risk, Trust & Safety, platform integrity, or enforcement engineering is preferred.
- 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; with a Ph.D., 2+ years of industry data science experience.
- 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 applying AI and large language models to practical data science workflows.
- Understanding of behavioral networks and large-scale activity patterns.
- Fluency in statistical analysis, Python or a similar programming language, and SQL.
- Strong technical leadership, communication, problem-solving, and stakeholder-influence skills.
Nice-to-haves
- Experience working across Ads, Safety, fraud, risk, or platform-integrity organizations.
- Experience with graph or network analysis, clustering, anomaly detection, or natural language processing.
Compensation and Benefits
- Base salary range: $190,800–$267,100 USD.
- Eligible for equity in the form of restricted stock units and, depending on the position, commission.
- Comprehensive healthcare and income replacement programs.
- 401(k) with employer match.
- Global benefits supporting workspace, professional development, and caregiving.
- Family planning support, gender-affirming care, and mental health and coaching benefits.
- Flexible vacation, paid volunteer time off, and paid parental leave.
Skills
Python, SQL, Statistical Analysis, Machine Learning, Generative AI, LLMs, Feature Engineering, Anomaly Detection, Network Analysis, Natural Language Processing, Streaming Data, Model Monitoring, Drift Detection, Threshold Calibration, Precision And Recall
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