Data Scientist

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🕒 Março 28

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CISPA Helmholtz Center for Information Security

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 2011

🔒 Cibersegurança

🤖 Inteligência Artificial

Cybersecurity • Artificial Intelligence • Research

O Centro de Segurança da Informação CISPA Helmholtz é uma instituição nacional de Grande Ciência dentro da Associação Helmholtz de Centros de Pesquisa Alemães. Ele explora a segurança da informação em todas as suas facetas para abordar os principais desafios em cibersegurança e inteligência artificial confiável. Reconhecido por sua pesquisa inovadora fundamental e aplicada, o CISPA visa cobrir todo o espectro, desde insights teóricos até estudos empíricos, posicionando-se como líder global no campo da cibersegurança e um campo de treinamento para a próxima geração de especialistas e líderes no domínio.

Descrição

• 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.

🎯 Requisitos

• 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).

🏖️ Benefícios

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