Data Scientist | Elastic (Security) Global Negotiation Guide

Negotiation DNA: Distributed-First Open-Source | Search & Security Platform | Public Company (NYSE: ESTC) | Liquid RSU Equity

Region	Base Salary	Stock (RSU/4yr)	Bonus	Total Comp
San Francisco / NYC	$162K–$212K	$118K–$205K	5–10%	$203K–$285K
Austin / Seattle	$152K–$202K	$108K–$190K	5–10%	$190K–$270K
London / Amsterdam	£123K–£161K / €129K–€170K	£89K–£156K / €94K–€164K	5–10%	£155K–£218K / €163K–€229K

Negotiation DNA Data Scientists at Elastic (Security) build the threat detection models, anomaly detection algorithms, and behavioral analytics that identify cyberattacks in real time — from insider threats and lateral movement to zero-day exploits and APT campaigns. This is one of the most technically demanding data science roles in cybersecurity: you're building ML models that must operate on high-volume, high-velocity security event streams with extremely low false-positive tolerance (security analysts are already overwhelmed with alerts). Elastic Security's ML capabilities are a key differentiator against Splunk and Microsoft Sentinel, making this role directly tied to competitive wins.

Level Mapping: Elastic Data Scientist IC3 = Google L4 DS = Meta E4 DS = CrowdStrike DS II = Splunk DS II

🏗️ Threat Detection ML & Behavioral Analytics Lever

In 2026, Elastic Security is investing in ML-powered threat detection that goes beyond rule-based alerts — behavioral anomaly detection that identifies unknown threats, user and entity behavior analytics (UEBA) that catches insider threats, and ML-driven alert prioritization that reduces analyst fatigue. Data Scientists building these models are creating the product features that differentiate Elastic Security in SIEM evaluations. Use this: "ML-powered threat detection is Elastic Security's competitive differentiator. Data Scientists who can build production anomaly detection, UEBA models, and intelligent alert prioritization on security event streams are the reason enterprises choose Elastic over rule-based legacy SIEMs."

Global Levers

1. Security ML Specialization: "Building ML models for cybersecurity — anomaly detection on security events, UEBA, threat hunting algorithms — requires rare domain expertise that goes beyond standard data science. I'd like $208K base."
2. Detection Efficacy Revenue Impact: "My models' detection accuracy directly determines whether Elastic wins SIEM evaluations. Better detection = more enterprise wins = ESTC stock growth. I'd like $200K RSU/4yr aligned with this impact."
3. Low False-Positive ML Expertise: "Security ML has uniquely stringent requirements — every false positive erodes analyst trust. My experience building [low-FP anomaly detection / precision-optimized classifiers / security-domain ML] is rare — $25K signing bonus."
4. Competing Security DS Offers: "I'm holding a CrowdStrike DS offer at $198K base / $205K RSU and a SentinelOne DS offer at $192K base / $195K RSU. I need $208K base and $200K RSU/4yr to choose Elastic."

Negotiate Up Strategy: "I want to build the threat detection ML that makes Elastic Security the most intelligent SIEM platform — behavioral anomaly detection, UEBA, and ML-driven alert prioritization that catches threats legacy systems miss. I'm holding a CrowdStrike DS offer at $198K base / $205K RSU/4yr. Elastic's search-native platform is the ideal foundation for security ML, and I want to build on it. I need $208K base, $200K RSU/4yr, and a $25K signing bonus. At $208K, I commit. My floor is $188K — below that, CrowdStrike's security data scale and equity win."

Evidence & Sources

• Levels.fyi Elastic data scientist compensation data (2025–2026)
• Glassdoor Elastic Data Scientist salary reports
• Blind verified security data science offer threads — SIEM and endpoint security companies (2025–2026)
• Elastic Security ML-powered detection feature documentation and MITRE evaluations