Discord

Discord

Manager, Scaled Abuse Countermeasures and Research

San Francisco Bay Area · Full-time

Sponsorship not specified$220k-$275kDetected 16 hours ago
PythonSQLMachine LearningData ScienceLLMsAgentic AIStatisticsCybersecurityIncident ResponseResearchLeadershipCommunicationCollaborationMentoring

About the role

  • SCAR combines rapid incident response with deep threat research and signal generation across bulk fake account creation, login abuse, spam, scams, fraud, and other high-volume threats.
  • This is an opportunity to reshape how the team operates: set a sharper strategy, stand up a structured research program, and lean heavily into ML and AI-powered automation to replace today's manual workflows.
  • This role reports to the Director of Safety Automation.

Responsibilities

  • Lead and grow the SCAR team, a group of Scaled Abuse Scientists who serve as Discord's first line of defense against bulk fake account creation, login abuse, spam, scams, fraud, and other high-volume threats.
  • Define scaled abuse north-star metrics and build a roadmap and prioritization framework that keeps the team focused on the highest-impact problems.
  • Partner cross-functionally with Product, Safety ML, Data Science, Policy, Legal, Trust & Safety, and Revenue, influencing safety-by-design decisions upstream so abuse is prevented, not just mitigated.
  • Demonstrated ability to drive ML and automation adoption within a Trust & Safety or operations context.
  • You don't need to build models yourself, but you need to understand them well enough to evaluate their quality, direct their development, and push a team toward automated solutions.

Requirements

  • 4+ years of experience working on Trust & Safety, fraud, anti-abuse, or a closely adjacent adversarial domain at a consumer-scale platform.
  • Strong analytical skills and fluency in SQL and Python for data investigation and pattern analysis (not full software engineering proficiency required).

Compensation

  • The US base salary range for this full-time position is $220,000 to $275,000 + equity + benefits.
  • Our salary ranges are determined by role and level.
  • Please note that the compensation details listed in US role postings reflect the base salary only, and do not include benefits.

Benefits

  • A relevant degree in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
  • Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training.

Company info

  • Set a vision for the team that leans heavily into automation - scale SCAR's impact by partnering with Safety ML on ML-driven detection and building AI-powered incident response workflows that increase the team's leverage and reduce manual work.
  • Close the loop with Safety ML so that SCAR's signals feed directly into model features and pipeline upgrades, and tactical wins translate into long-term, automated countermeasures.
  • Coach, hire, and grow Scaled Abuse Scientists.
  • Communicate attacker dynamics, economic incentives, and trade-offs clearly to senior leadership.
  • 2+ years of people management experience leading technical teams, including engineers, ML engineers, data scientists, or applied researchers, or an equivalent track record of leading, mentoring, or coordinating technical work in a way that has prepared you to step into this role.
  • Demonstrated ability to drive ML and automation adoption within a Trust & Safety or operations context. You don't need to build models yourself, but you need to understand them well enough to evaluate their quality, direct their development, and push a team toward automated solutions.
  • Ability to think from first principles and apply behavioral-economics reasoning to adversarial systems: why are attackers doing this, what are the incentives, and what is the most cost-effective way to break their economics.
  • Excellent communication and cross-functional collaboration skills, with a history of partnering effectively across engineering, ML, data science, policy, legal, and product teams.
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This listing is sourced directly from Discord's careers page and normalized into a canonical job model.