Senior Data Scientist, Ads Integrity

This is a fully remote position, open to applicants in United States.

📋 Description

• Spearhead the strategy for measuring and detecting ad fraud by establishing fraud taxonomies, labels, sampling plans, metrics, and evaluation frameworks.

• Examine extensive and intricate datasets along with behavioral networks to identify emerging fraud patterns, assess impact, determine root causes, and convert findings into detection and enforcement specifications.

• Create and implement scalable ad fraud detection and enforcement pipelines in collaboration with Engineering and Machine Learning teams.

• Manage the entire detection lifecycle, which encompasses backtesting, threshold calibration, offline and online evaluations, launch validation, experimentation, monitoring, drift detection, incident response, rollback, and retirement.

• Develop and sustain statistical, machine learning, and GenAI-enabled models or prototypes.

• Weigh fraud loss, platform and advertiser risk, customer experience, false-positive costs, operational capacity, and business objectives when suggesting thresholds and enforcement strategies.

• Collaborate across Ads and Safety to influence strategy and roadmaps, enhance data foundations, bridge policy and enforcement gaps, and comply with governance and compliance standards.

• Convert complex analyses into straightforward narratives and actionable recommendations for both technical and non-technical audiences, including senior leadership.

• Guide and mentor fellow data scientists and analysts.

⛳️ Requirements

• Relevant experience in Data Science, Applied Science, or a similar quantitative role, ideally in ad 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 discipline.

• A minimum of 4 years of industry data science experience with an M.S. degree; or 2 years with a Ph.D.

• Proven experience in constructing or significantly influencing production detection and automated enforcement pipelines, including batch or streaming data, feature engineering, rules or models, decision-making, monitoring, and feedback mechanisms.

• Strong expertise in fraud or abuse detection methodologies and evaluations, including label design, precision and recall trade-offs, calibration, threshold selection, false-positive analysis, drift detection, and adversarial adaptation.

• Experience collaborating with Product and Engineering teams to translate analyses and prototypes into dependable production systems.

• Familiarity with applying AI and large language models (LLMs) in practical data science processes.

• Understanding of complex behavioral networks or large-scale activity patterns; experience in graph or network analysis, clustering, anomaly detection, or natural language processing is a plus.

• Proficiency in statistical analysis, Python or a comparable programming language, and SQL.

• Capability to work independently across complex data systems and unfamiliar codebases.

• Aptitude for addressing ambiguously defined problems and progressing from investigation to scalable, reusable solutions.

• Strong technical leadership and communication skills, with a history of influencing cross-functional roadmaps and aligning stakeholders.

🏝️ Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs.

• 401k with Employer Match.

• Global Benefit programs that align with your lifestyle, including workspace, professional development, and caregiving support.

• Family Planning Support.

• Gender-Affirming Care.

• Mental Health & Coaching Benefits.

• Flexible Vacation & Paid Volunteer Time Off.

• Generous Paid Parental Leave.

• Equity in the form of restricted stock units.

• Medical, dental, and vision insurance.

• Option to opt out of interview recording, transcription, and summarization.