Manager, Scaled Abuse Countermeasures and Research

About This RoleAI processing…

Key Responsibilities

• 1
  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.
• 2
  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.
• 3
  Define scaled abuse north-star metrics and build a roadmap and prioritization framework that keeps the team focused on the highest-impact problems.
• 4
  Stand up a research program where the team operates as scientists: surface the most important questions about attacker operations and signals, then systematically answer them through structured research and experimentation.
• 5
  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.
• 6
  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.
• 7
  Coach, hire, and grow Scaled Abuse Scientists.
• 8
  Communicate attacker dynamics, economic incentives, and trade-offs clearly to senior leadership.

Requirements

• 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.
• 4+ years of experience working on Trust & Safety, fraud, anti-abuse, or a closely adjacent adversarial domain at a consumer-scale platform.
• 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.
• Strong analytical skills and fluency in SQL and Python for data investigation and pattern analysis (not full software engineering proficiency required).
• 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.
• A growth mindset: you seek feedback, reflect on decisions, and actively help your team do the same.

Perks & Benefits