Data Scientist

Job not on LinkedIn

🕒 March 28

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

CISPA Helmholtz Center for Information Security

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2011

🔒 Cybersecurity

🤖 Artificial Intelligence

Cybersecurity • Artificial Intelligence • Research

CISPA Helmholtz Center for Information Security is a national Big Science institution within the Helmholtz Association of German Research Centers. It explores information security in all its facets to address major challenges in cybersecurity and trustworthy artificial intelligence. Renowned for its cutting-edge foundational and applied research, CISPA aims to cover the full spectrum from theoretical insights to empirical studies, positioning itself as a global leader in the cybersecurity field and a training ground for the next generation of experts and leaders in the domain.

📋 Description

• 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.

🎯 Requirements

• 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).

🏖️ Benefits

• Familiarity with complex cybersecurity environments and data sets is a plus here.