Senior Data Scientist (Cyber Risk Analytics)

Location: Madrid, Spain; hybrid work with days at the ING MAD office

Company

hirify.global Spain is a technology and operations hub within ING, developing scalable and secure solutions for banking customers across more than 38 countries.

What you will do

• Design, develop, and maintain AI and machine learning models for quantifying, assessing, and monitoring third-party cyber risk.
• Build dynamic vendor risk scoring by integrating risk assessments, security posture indicators, threat intelligence, and other internal and external data.
• Apply predictive analytics, anomaly detection, and other advanced analytical techniques to improve risk identification and prioritisation.
• Develop agentic AI solutions using LLMs, tool integration, and automated reasoning workflows for risk assessment and decision support.
• Own the full model lifecycle, including data preparation, feature engineering, validation, deployment, monitoring, recalibration, reproducibility, and MLOps.
• Collaborate with TPCRM specialists, cybersecurity teams, and data engineers while communicating model performance and risk insights to technical and non-technical stakeholders.

Requirements

• Master’s degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Physics, or a related quantitative discipline.
• 7+ years of professional experience in data science, advanced analytics, statistical modelling, or machine learning, preferably delivering solutions into production.
• Familiarity with generative AI, LLM-based applications, and emerging agentic AI architectures, including their practical applications and limitations.
• Strong proficiency in Python and SQL, including hands-on experience with common data science and machine learning libraries.
• Experience developing, validating, and maintaining statistical and machine learning models throughout their lifecycle.
• Strong understanding of statistical methods, predictive modelling, feature engineering, model evaluation, and uncertainty.
• Experience with complex, heterogeneous datasets and translating ambiguous business problems into structured analytical approaches.
• Familiarity with model deployment, monitoring, reproducibility, and MLOps practices.
• Ability to independently lead technical initiatives, collaborate with multidisciplinary teams, solve analytical problems, and explain complex modelling approaches to non-technical audiences.

Nice to have

• Experience developing and deploying AI solutions in heavily regulated environments, particularly financial services.
• Familiarity with cybersecurity, cyber risk management, third-party risk management, or cyber risk metrics.
• Experience with risk scoring methodologies, probabilistic modelling, anomaly detection, or decision-support systems.
• Familiarity with model risk management, explainability, regulatory expectations, and governance frameworks.
• Experience with cloud-based data and analytics platforms and production-grade data pipelines.

Culture & Benefits

• Hybrid flexibility and accountability, with employees determining office and home-working days together.
• Health insurance for the employee and family, life insurance, and a pension plan available after one month.
• Restaurant card, transport allowance, flexible remuneration, and support for nursery, transport, and training services.
• Office facilities include electric mobility solutions, a doctor, hairdresser, gym, and personal services.
• Tech-first, agile, collaborative, international environment focused on continuous learning, diversity, inclusion, sustainability, and customer impact.