Cybersecurity Risk Quantification Leader at HSBC Technology Poland

4 days ago


Kraków, Lesser Poland, Czech Republic HSBC Technology Poland Full time
Job Description

HSBC Technology Poland is seeking a seasoned professional to lead our Cybersecurity Risk Quantification initiatives.

About the Role

The Head of Cybersecurity Risk Quantification will play a critical role in shaping our cybersecurity strategy, partnering with key stakeholders across IT, operations, compliance, and risk management.

This individual will be responsible for leading our cybersecurity risk quantification initiatives, ensuring that the organization's risk posture is clearly understood, measurable, and actionable.

They will apply cutting-edge probabilistic risk assessment methodologies, lead a talented team of risk professionals, data scientists, threat hunters, and data engineers, and drive the development of innovative risk models that enhance our ability to forecast and mitigate cybersecurity risks.

Key Responsibilities
  • Lead and mentor a high-performing, multidisciplinary team of cybersecurity risk professionals, including data scientists, threat hunters, and data engineers.
  • Collaborate with executive leadership to integrate quantitative risk insights into enterprise risk management frameworks and decision-making processes.
  • Present findings and recommendations to senior leadership in a clear, data-driven manner.
  • Act as a key advisor to the regional and business lines on emerging cybersecurity risks and their quantifiable impacts.
  • Oversee the integration of diverse datasets (threat intelligence, financial data, logs etc.) to enhance risk assessment accuracy.
  • Supervise the development and optimization of Python-based libraries and tools for cybersecurity risk modelling.
  • Design and implement probabilistic risk assessment models to quantify cybersecurity threats, vulnerabilities, and impacts on the organization using advanced statistical techniques.
Requirements
  • Proven experience in quantitative risk modelling in a regulated industry or in actuarial sciences.
  • Degree in math, physics, statistics/data science, computer science, or a related engineering field.
  • Hands-on experience in developing decision analysis models.
  • Strong experience in developing complex numerical solutions using Python.
  • Advanced statistical and data analysis skills are essential.
  • Proven experience in deploying Machine Learning models into production, with strong familiarity with best MLOps practices.
  • Hands-on experience with data engineering tools preferably Spark, Lakehouse architecture, and Databricks.
  • Knowledge of good software engineering practices.
What We Offer

We offer a range of benefits and opportunities to support your career growth and well-being:

  • Training budget.
  • Private healthcare.
  • Flat structure.
  • International projects.
  • Multisport card.


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