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Empirical Security · 2 days ago

Forward Deployed Data Scientist

United States

Full-time

Remote

Mid, Senior Level

Empirical Security is seeking an experienced Security Data Scientist to join their innovative Forward Deployed Data Scientist team focused on building the next generation of cybersecurity vulnerability models. The role involves collaborating closely with customers and internal teams to operationalize vulnerability models and ensure they meet customer needs while maintaining data integrity and security.

Artificial Intelligence (AI)Cyber SecurityNetwork Security

Responsibilities

Collaborate closely with our customers, engineering, product, and security teams to operationalize vulnerability models, ensuring scalability, reliability, and alignment with customer needs

Lead discovery and prioritization of customer security data sources (asset inventory, vuln scanners, EDR, IAM, CMDB, cloud posture, ticketing, external attack surface, threat intel), including feasibility, value, and effort trade-offs

Apply exposure-management domain expertise to ensure data supports actionable use cases (attack surface reduction, vulnerability prioritization, remediation workflows, risk acceptance, SLA tracking)

Partner with engineering to design and validate ingestion pipelines (APIs, exports, streaming/batch), ensuring reliability, observability, and secure handling of customer data

Perform pragmatic data analysis to diagnose data issues and quantify impact (completeness, accuracy, timeliness, consistency), and recommend remediation steps to customers and internal teams

Define and maintain customer-facing technical documentation: integration guides, data dictionaries, validation checklists, and runbooks for common ingestion and modeling issues

Collect, clean, explore, analyze, and normalize various security data sources

Stay current on exposure-management practices, vulnerability intelligence, attacker tradecraft, and the relevant vendor ecosystem to inform integrations and customer guidance

Qualification

PythonSQLSecurity data integrationExposure management expertiseData analysisEnterprise security architecturePragmatic analyticsTechnical communicationTechnical collaborationCommon security telemetry

Required

Baseline engineering hygiene (Python/SQL comfort, APIs and data formats, Git/version control, and an appreciation for reliability/observability and secure data handling)

Enterprise security engineering / architecture fluency (security controls, reference architectures, trade-offs, and how security capabilities integrate into real-world enterprise environments)

Exposure and vulnerability management expertise (asset-centric thinking, prioritization workflows, remediation SLAs, exception handling, and common program maturity patterns)

Security data integration and normalization skills (ability to evaluate customer data sources, assess data quality, define mapping/normalization, and drive onboarding priorities)

Strong customer-facing technical communication (requirements discovery, explaining complex technical concepts clearly, running workshops, and producing crisp technical documentation)

Working knowledge of common security telemetry and systems (e.g., vulnerability scanners, EDR, IAM, CMDB, ticketing/ITSM, cloud security, external attack surface—enough to ask the right questions and validate data fitness)

Pragmatic analytics capability (comfortable with basic statistics, exploratory analysis, and sanity-checking model outputs; can quantify uncertainty and limitations without being a deep ML specialist)

Technical collaboration across engineering and data science (can translate customer needs into technical requirements, partner on pipeline design, and unblock implementation details)