Senior Data Scientist - Cyber Risk Analytics

ING Bank Personeel; Madrid; On-site; EUR 90,000 - 130,000; Full time

Job summary

ING Hubs Spain is seeking a Senior Data Scientist to join the Security Analytics team, focusing on Third-Party Cyber Risk Management. You will design and operationalise AI-driven solutions, build continuous risk models, and deploy end-to-end analytics across multiple data sources and stakeholders.

The role emphasizes deploying robust, explainable models, proactive risk scoring, and collaboration with cybersecurity teams to strengthen vendor risk management and regulatory compliance.

Qualifications

• Master's degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Physics or related quantitative discipline
• 7+ years of professional experience in data science, analytics, ML or related field
• Familiarity with generative AI, LLM-based applications and agentic AI architectures
• Strong Python and SQL skills with hands-on experience in ML libraries
• Experience building, validating and deploying models through their lifecycle
• Strong statistical methods, predictive modelling and feature engineering knowledge
• Experience with heterogeneous data and translating business problems into analytical approaches
• Familiarity with model governance, reproducibility and MLOps practices
• Ability to lead technical initiatives while collaborating with multidisciplinary teams
• Strong analytical and communication skills to explain complex modelling to non-technical audiences

Responsibilities

• Design, develop and maintain AI models to quantify, assess and monitor third-party cyber risk
• Develop dynamic vendor risk scoring by integrating multiple data sources
• Apply predictive analytics, anomaly detection and other advanced techniques
• Explore agentic AI solutions using LLMs and automated reasoning workflows
• Own the model lifecycle from data prep to recalibration
• Collaborate with TPCRM specialists, cybersecurity teams and data engineers
• Ensure models are robust, explainable and compliant with governance
• Communicate findings and risk insights to technical and non-technical stakeholders
• Identify new data science use cases to enhance third-party risk management

Skills

Python

SQL

Statistical modelling

Machine learning

Communication

Problem solving

Leadership

Regulatory awareness

Education

Master's degree or PhD in Data Science or related quantitative discipline

Tools

Git

Cloud platforms (AWS/GCP)

MLOps

Data visualization tools

Job description

Senior Data Scientist - Cyber Risk Analytics

At ING Hubs Spain we are looking for a Senior Data Scientist - Cyber Risk Analytics

Your role and work environment:

We are looking for an experienced Data Scientist to join our Security Analytics team, with a dedicated focus on Third-Party Cyber Risk Management (TPCRM). The role will be responsible for designing, developing and operationalising advanced analytical, machine learning and AI-driven solutions that strengthen our ability to assess, monitor and anticipate cyber risks associated with third-party vendors.

A key initial focus will be the development of continuous risk models and dynamic vendor risk scoring, moving beyond periodic assessments towards more proactive, data-driven risk management. Working closely with TPCRM specialists and other cybersecurity stakeholders, you will take end‑to‑end ownership of an expanding portfolio of analytical and AI solutions, from initial design and development through to deployment and continuous improvement.

Responsibilities:

• Design, develop and maintain AI models to quantify, assess and monitor third-party cyber risk.
• Develop dynamic vendor risk scoring capabilities by integrating multiple internal and external data sources, including risk assessments, security posture indicators and threat intelligence.
• Identify opportunities to apply predictive analytics, anomaly detection and other advanced analytical techniques to improve risk identification and prioritisation.
• Explore and develop agentic AI solutions, leveraging large language models (LLMs), tool integration and automated reasoning workflows to enhance risk assessment, intelligence gathering and decision support.
• Take end‑to‑end ownership of the model lifecycle, including data preparation, feature engineering, model development, validation, deployment, monitoring and recalibration.
• Collaborate with TPCRM specialists, cybersecurity teams and data engineers to translate complex risk challenges into scalable analytical solutions.
• Ensure models are robust, explainable and aligned with internal model governance, data quality and regulatory requirements.
• Communicate analytical findings, model performance and risk insights effectively to technical and non‑technical stakeholders.
• Continuously identify and develop new data science use cases to enhance third-party cyber risk management capabilities.

Qualifications:

• 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 with experience 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.
• Proven 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 working 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 while collaborating effectively with multidisciplinary teams.
• Strong analytical, problem‑solving and communication skills, with the ability to explain complex modelling approaches to non‑technical audiences.

Preferred Qualifications:

• Experience developing and deploying AI solutions in heavily regulated environments, particularly financial services.
• Familiarity with cybersecurity concepts, cyber risk management, third‑party risk management or cyber risk metrics (preferred).
• 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.

What do we offer?

The time you spend at work, the challenges you face or the lessons you get are very important, but... What about your personal life? At ING we want your work to fulfill you in every way, and that is why we take care of even the smallest details.