Senior Data Scientist, Ads Integrity
New
J
JobgetherAdvertising Technology
Fully remote-friendly work within the United States.Full-TimeSenior
SalaryBase salary range of $190,800–$267,100 USD, depending on factors such as skills, experience, credentials, and location.
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Job Details
- Experience
- 4+ years of industry data science experience with a Master's, or 2+ years with a Ph.D.
- Required Skills
- PythonSQLMachine LearningNLPGenerative AI
Requirements
- Master's degree in Statistics, Economics, Computer Science, Applied Mathematics, or another quantitative discipline with 4+ years of industry data science experience, or a Ph.D. with 2+ years of experience.
- Relevant experience in data science, ideally within ads fraud, financial fraud, account risk, or Trust & Safety.
- Experience building or shaping production detection and automated enforcement systems, including batch or streaming data and feedback loops.
- Strong knowledge of fraud and abuse detection methodologies, including precision and recall tradeoffs and adversarial adaptation.
- Proven ability to partner with Product and Engineering to turn prototypes into reliable, scalable production systems.
- Experience applying AI and large language models to practical data science use cases.
- Understanding of behavioral networks with experience in graph analysis, clustering, anomaly detection, or NLP.
- Advanced proficiency in statistical analysis, Python, and SQL.
- Ability to independently navigate complex data systems and unfamiliar codebases.
- Strong analytical and problem-solving abilities for breaking ambiguous problems into actionable components.
- Technical leadership and stakeholder management skills with the ability to influence cross-functional roadmaps.
Responsibilities
- Lead the measurement and detection strategy for ads fraud by defining fraud taxonomies, labels, sampling methodologies, metrics, and rigorous evaluation frameworks.
- Analyze large-scale datasets and behavioral networks to identify emerging fraud patterns and translate insights into actionable requirements.
- Partner with Engineering and Machine Learning teams to design and develop scalable fraud detection and automated enforcement pipelines.
- Own the complete detection lifecycle, including backtesting, threshold calibration, experimentation, and drift detection.
- Develop and evaluate statistical, machine learning, and GenAI-enabled models to improve fraud detection and investigation efficiency.
- Balance fraud losses, platform risk, and operational capacity when recommending enforcement strategies.
- Collaborate across Ads and Safety functions to shape strategic priorities, roadmaps, and data foundations.
- Translate complex analytical findings into clear insights for technical and non-technical senior leadership.
- Mentor data scientists and analysts while contributing technical leadership.
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