By ensuring advertisers reach real customers and understand the true value of their campaigns.

You will work at the intersection of fraud detection and attribution, analyzing large‑scale behavioral data, identifying sophisticated fraud patterns, and helping the organization interpret attribution data correctly. A key part of your work is explaining what the data can and cannot tell us, guiding product, PMs, and business partners toward decisions rooted in statistical rigor and sound reasoning. Your insights strengthen trust in our entire marketing & advertising ecosystem.

This role demands an unflinching commitment towards the realities of fraud detection and attribution modeling. You’ll face datasets that are messy, adversarial, and often riddled with bias—there are no shortcuts or easy answers. Every day brings new tactics from fraud actors and shifting market dynamics, requiring a commitment to transparency and rigorous analysis.

Complexity is the norm: distinguishing genuine customer behavior from noise or malicious signals is rarely straightforward. You’ll investigate bot activity, attack surfaces, and suspicious cohorts, and you’ll be expected to clearly articulate how attribution data should—and should not—be interpreted. Quantifying uncertainty, bias, and econometric effects is a central part of the job, as is validating hypotheses with statistical rigor and improving attribution logic using advanced modeling techniques.

Success in this role is rooted in analytical independence and a collaborative mindset. You’ll bridge gaps across teams, translating complex findings into actionable insights and fostering a culture of transparency and trust. Leadership here is demonstrated not by titles, but by the courage to confront complexity, communicate honestly, and drive principled decisions that shape our advertising ecosystem.

You provide the analytical depth and cross‑team influence needed to keep our platform safe and accurately measured. Fraud actors evolve rapidly, and attribution accuracy requires both mathematical skill and clear stakeholder guidance.

You will combine technical modeling with strong communication and organizational influence, helping teams understand how to interpret data, weigh model choices, and align around the right direction.

You shape both our fraud detection evolution and our attribution logic, not only through modeling but by driving the conversations that lead to the right strategic choices.

You’ll need to:

• separate genuine customer behavior from malicious or noisy signals
• investigate attack surfaces, bot behavior, and suspicious cohorts
• quantify uncertainties, biases, and econometric effects
• validate hypotheses using statistical reasoning
• improve attribution logic using statistical, econometric, ML, or neural network techniques
• guide PMs and business in understanding trade‑offs
• choose between ML, econometric models, or neural networks — and articulate why

You’ll work closely with cross-functional teams including data science, product, engineering, and business stakeholders, acting as a key resource for fraud detection and attribution analytics.

Expect a blend of hands-on data analysis, model development, team meetings, and stakeholder presentations, with a focus on driving actionable insights and platform safety.

You bring depth in statistics, econometrics, machine learning, and analytical investigation.
You are energized by:

• exploring ambiguous or messy data,
• reasoning economically about value, uncertainty, and incentives,
• separating signal from noise,
• guiding teams toward the right modeling choices,
• and independently diving into large datasets to uncover actionable insights.

Your work directly influences platform trust, advertiser value measurement, and detection quality.

You’ll join the Reliable product group within Marketing & Advertising, working closely with Engineering, Product, Analytics and Business teams. The team serves as the primary ingestion point for interaction data in Marketing & Advertising, holding a business-critical role in ensuring transparency to our advertisers.

Overview of hard skills

To be successful in this role, you need:

• Experience shaping analytical solutions and guiding organizational decision‑making
• Strong skills in statistics and/or econometrics
• Experience with ML (any framework) — anomaly detection, fraud detection, or adversarial modeling a plus
• Familiarity with attribution modeling, causal inference, or bias‑correction techniques
• Proficiency in SQL (BigQuery) and Python
• Ability to explain data and modeling choices to non‑technical stakeholders
• Strong communication and data visualization skills
• Curiosity and passion for applied ML, adversarial thinking, and complex data challenges correction‑ techniques