CISPA Helmholtz Center for Information Security

Data Scientist

CISPA Helmholtz Center for Information Security

full-time

Posted on:

Location Type: Remote

Location: United States

Visit company website

Explore more

✨ AI Apply
Apply

Tech Stack

CloudITSMPythonSQL

About the role

  • 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.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonSQLAPIsdata formatsGitdata analysisdata integrationdata normalizationvulnerability managementsecurity engineering
Soft Skills
customer-facing communicationrequirements discoverytechnical documentationcollaborationproblem-solvinganalytical thinkingworkshop facilitationclear explanation of technical conceptsprioritizationtechnical translation