# Senior Data Scientist – Cyber Risk Analytics at ING

ING is hiring a Senior Data Scientist – Cyber Risk Analytics. Level rates it Builds AI ●●●●; you can [apply on ING](https://jobsbylevel.com/go/c4ea426a-c233-4e43-b140-9ee61882ecd9?utm_medium=ai-agent).

AI Level 4, AI centrality 89 out of 100. Madrid (Hubs Spain).

## Details

- Company: [ING](https://jobsbylevel.com/companies/ing)
- AI level: AI Level 4 (score 89 out of 100)
- Location: Madrid (Hubs Spain)
- Posted: October 30, 2026
- Apply: https://jobsbylevel.com/go/c4ea426a-c233-4e43-b140-9ee61882ecd9?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-data-scientist-cyber-risk-analytics-at-ing-b4fb15

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## Description

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? Want to know if this job is made for you? These are some of the qualities that we value the most for this role. And if the shoe fits… don’t hesitate to apply! 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

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