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Open nowFirst seen 3 hours ago

Senior Director, Data Science - Security Analytics

Meta1,228 open roles

Where
Menlo Park, CA
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Your applicationOpen nowSenior Director, Data Science - Security AnalyticsMeta · Menlo Park, CA

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The clock on this job

Early applications get read.

12.7% of postings close within 7 days. Measured by our own scanner across the market. Meta postings stay open a median of 33 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.5%3 days
  3. 12.7%7 days
  4. 20.4%14 days
  5. 35.6%30 days
  6. 58.7%60 days
This job: first seen 3 hours ago

Meta median: 33 days open

The posting

Meta is seeking an experienced analytics leader to build and run Security Analytics for Central Security. You will lead Data Science and Data Engineering as one integrated function and serve as the primary data partner to Meta's Chief Information Security Officer and his leadership team. This is a high-impact, high-visibility leadership role where you'll turn Security's strategy, risk, and execution into a trusted data system for decisions.

Security is operating through a period of rapid change. AI is expanding the attack surface, agentic systems are changing how security work gets done, and leadership needs a clearer view of where Meta is exposed, whether controls work, and where to invest next. You'll own the measurement system that answers those questions.

The Challenge -Create one integrated Security Analytics function that makes Meta's security posture legible and actionable across a fast-paced, rapidly evolving threat landscape -Establish a persistent model of Security health that connects risk to controls, investment, and outcomes -Build the DS and DE organization, standards, and data foundations needed to sustain it

Ideal Candidate -A builder and scaler who thrives in extreme ambiguity — you have created measurement systems and operating models in undefined spaces, then scaled the teams and foundations behind them -Leadership across DS + DE — you set one vision across insight, measurement, instrumentation, and data infrastructure, and you know where each discipline should lead -Security, risk, and systems thinking — you reason from threats through controls to residual risk, distinguish activity from outcomes, and make uncertainty visible without making the output unusable -AI-native leadership — you use AI to change how analytics and security work gets done, not just to make the existing process marginally faster Executive presence with the credibility to shape the questions, not just answer them

Responsibilities

  • Serve as the analytics partner to Meta's CISO — translate Security's highest-priority questions into an analytics strategy, bring independent judgment to leadership decisions, and give a clear read on what is working, what is not, and what should change.
  • Build a durable view of Security health across Residual Risk and Friction: the risk remaining after controls, and the cost those controls impose on Meta's builders, products, and users.
  • Enable posture comparison over time as threats, technologies, and controls evolve — surfacing where stronger security improves outcomes versus where it creates avoidable friction.
  • Identify which controls are most problematic and why (coverage, effectiveness, performance, adoption, developer and user experience), and use that evidence to determine which controls should be redesigned, expanded, automated, or retired.
  • Build the logging, instrumentation, semantic definitions, pipelines, and governed data products that make Security analytics trustworthy and AI-ready.
  • Enable product and engineering teams across Meta to identify risk, understand the controls that apply, and self-serve mitigations in their own technology stacks.
  • Set one strategy and operating model across Data Science and Data Engineering, and develop the team, metrics map, dashboard suite, and operating rhythm that make data a routine input to Security strategy and resource allocation.
  • Build strong relationships with XFN partners across Security, Infrastructure, Product, and Engineering orgs.

Minimum Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 15+ years of experience in analytics, data science, or data engineering, including 8+ years leading teams
  • Experience leading both Data Science and Data Engineering functions (or demonstrated ability to set strategy across both)
  • Experience building measurement frameworks and metrics systems that drive executive-level decisions
  • Experience in Security, Integrity, Privacy, Infrastructure, or another technically deep risk domain — or demonstrated ability to ramp quickly and earn credibility in one
  • Experience partnering directly with senior executives and operating as a member of a leadership staff
  • Experience building data platforms, instrumentation, and governed data products at scale

Preferred Qualifications

  • Experience with security controls, threat modeling, or risk quantification
  • Experience applying AI/LLM-based systems to analytics workflows at scale

US: $314,000/year to $361,000/year + bonus + equity

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