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.

Your key responsibilities:

As a Data Scientist you will:

- 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.

What are we looking for?

- 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 pers

📌 Senior Data Scientist (España)
🏢 Ing
📍 España