Risk AI Data Scientist
AI in this role
We are looking for a Risk AI Data Scientist to drive the integration of advanced AI capabilities into the bank’s overall risk management (out of which Credit risk model maintenance is one of them).
In this role, you operate in the intersection of risk management practices (amongst which credit risk modelling), model lifecycle governance, and advanced AI (LLMs, NLP, Agentic workflows). You will not only build and steer these solutions; you will help design the cognitive layer of the bank’s risk management environment, turning cutting-edge AI into production-ready, compliant solutions.
The team
The mission of Integrated Risk is focused on providing risk identification, aggregation and insight capabilities at Group level across the various Risk domains. The team department is using those capabilities across the various risk functions, to assume a general oversight of risk governance, policies and frameworks, and to steer group-wide model and implementation activities across locations.
The Bank-wide Credit Risk Models department is responsible for the management of Wholesale Banking (WB) IRB and IFRS9 and the Bank-wide Credit Risk Economic Capital models — including their development, monitoring, and advisory support to the business — in cooperation with relevant stakeholders. All the models in scope are groupwide, managed and developed centrally and consistently applied across all ING’s locations.
Roles and responsibilities
What will you do?
Develop custom models by fine-tuning open-weights models (e.g., Llama, Mistral) on GCP GPUs to understand the specific nuances of risk management in wholesale/retail banking, credit policies, and financial risk (exploration, production and scaling).
Evaluate and monitor GenAI systems in this context (e.g. hallucinations, quality, drifting).
Architect Retrieval-Augmented Generation (RAG) systems to enable interaction with internal policy documents, regulations and other documentation in different formats with high precision.
Work with large-scale unstructured data (documents, PDFs, OCR) as a core part of the role.
Design and implement agentic AI workflows (e.g. LangChain/LangGraph) where AI components plan, reason, and execute multi-step tasks to support risk managers.
Build NLP pipelines to extract complex signals (e.g. transaction patterns, legal clauses) from unstructured text and convert them into usable features.
Write clean, modular Python code in Azure DevOps ensuring models are testable, reproducible, and ready for deployment.
Align with model suites across different risk domains to understand and create added value in AI-powered lifecycle management.
How to succeed
We hire smart people like you for your potential. Our biggest expectation is that you’ll stay curious. Keep learning. Take on responsibility. In return, we’ll back you to develop into an even more awesome version of yourself.
Education & experience
Master’s degree in mathematics, economics or equivalent
7+ year experience in risk management (experience with risk modelling is a plus)
Core technical stack
Advanced Python, SQL, PyTorch/TensorFlow (SAS is an advantage)
GenAI & data capabilities
HuggingFace (Transformers, PEFT), LangChain/LlamaIndex, Vector Stores (FAISS/Vertex Search), designing and optimising RAG pipelines
Experience in manipulating and governing structured and unstructured data for risk management purposes
Platforms & engineering
Google Cloud Platform (Vertex AI, Workbench)
Strong experience with Azure DevOps (Git, Pipelines)
Experience with end-to-end pipelines (data → model → deployment)
Way of working
Experience working in Agile/Scrum teams
You understand the “You Build It, You Run It” philosophy
Governance & mindset
Knowledgeable on AI (risk) governance
Out-of-the-box, inquisitive, strategic thinking
Strong risk management mindset and technically fully mature credit risk modelling skills
Strong communication skills (internally and externally), with the capability of translating complex matters into simple language
“Make it happen” mentality
Rewards and benefits
We want to make sure that it’s possible for you to strike the right balance between your career and your private life. Find out more about our employment conditions.
The benefits of working with us at ING include:
25-28 vacation days depending on contract
Pension scheme
13th month salary
8% Holiday payment
Hybrid working
Personal growth and challenging work with endless possibilities
An informal working environment with innovative colleagues
About us
Curious about how ING empowers people and businesses to move forward?
Discover what we do and what we can offer you.
Questions?
Please visit our Frequently Asked Questions section to find some answers on questions you might have. You can also contact the recruiter attached to the advertisement. Want to apply directly? Please upload your CV and motivation letter by clicking the ‘Apply’ button.
How we score this
Risk AI Data Scientist at ING scores 96 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 4. Building AI systems is the job itself: without AI, the role would not exist.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Walk me through how you've used Mistral in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Rag, AI Agents, Fine Tuning, Nlp, and Mistral. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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