Senior Data Scientist, Ads Integrity

Reddit

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$190,800–$267,100 / yr
Posted
15 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $203k
This role $229k
$144k most similar roles pay here $280k

This role pays more than 69% of similar roles. Most pay $162,000–$243,600 — the shaded band above. At the midpoint, this role pays about $229k versus about $203k for comparable roles.

Based on 240 similar postings.

Employer

About Reddit

Reddit is a social news aggregation and discussion platform where users share content, vote on posts, and engage in community conversations across thousands of interest-based forums called subreddits.

Reddit currently has 77 open roles on FindRole.

Listed pay typically runs $217,000–$303,400 across 77 roles with salary data.

Most-posted roles

View all roles at Reddit

At a glance

TL;DR · Senior Data Scientist, Ads Integrity

Senior Data Scientist, Ads Integrity joins the Safety team to lead ads fraud detection and scaled enforcement efforts. This role involves defining fraud taxonomies, measurement standards, and evaluation frameworks while analyzing complex datasets and behavior networks to uncover emerging fraud patterns. The successful candidate will design and develop scalable enforcement pipelines, including feature generation, rule-based systems, and machine learning models for real-time scoring and investigation automation. Key responsibilities include managing the full detection lifecycle, from backtesting and threshold calibration to monitoring and drift detection. The role requires proficiency in Python, SQL, and statistical analysis, with specific experience applying large language models and graph analysis to identify risks. This position addresses the critical challenge of protecting users from harmful content and fraudulent behavior while balancing platform safety, advertiser risk, and user experience within a complex advertising ecosystem.

What does a Data Scientist earn in Remote?

Median $217225 from 40 postings across 18 companies.

See salary data

What you'll do

  • Define fraud taxonomies, sampling plans, metrics, and evaluation frameworks to make ads fraud performance measurable.
  • Analyze complex datasets and behavior networks to identify emerging fraud patterns and root causes.
  • Develop scalable detection and enforcement pipelines including feature generation, rules, models, and real-time scoring.
  • Manage the full detection lifecycle including backtesting, threshold calibration, monitoring, and incident response.
  • Build and maintain statistical, machine learning, and GenAI-enabled models to improve fraud detection and investigator efficiency.
  • Balance fraud loss, platform risk, and customer experience when recommending enforcement strategies and thresholds.
  • Partner with cross-functional teams to shape product roadmaps and ensure compliance with governance standards.
  • Translate complex technical analyses into actionable narratives for both technical and non-technical stakeholders.

What we're looking for

  • Ph.D. or M.S. degree in Statistics, Economics, Computer Science, Applied Mathematics, or another quantitative field.
  • 4+ years of industry data science experience for candidates with an M.S. degree.
  • 2+ years of industry data science experience for candidates with a Ph.D. degree.
  • Relevant experience in Data Science, Applied Science, or related roles in ads fraud, financial fraud, trust and safety, or platform integrity.
  • Experience building production detection and automated enforcement pipelines including feature engineering, rules, models, and monitoring.
  • Proficiency in fraud/abuse detection methods such as label design, precision/recall tradeoffs, calibration, and drift detection.
  • Experience applying AI and large language models (LLMs) to practical data science workflows like threat discovery or content classification.
  • Fluency in statistical analysis, Python, and SQL with the ability to work across complex data systems.

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