Risk AI Data Scientist
AI in this role
ING Hubs Poland is hiring!
The expected salary for this position: 13000 – 22000 PLN
The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.
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.
We are looking for you, if you have:
- Master’s degree in mathematics, economics or equivalent,
- 7+ year experience in risk management (experience with risk modelling is a plus),
- Advanced Python, SQL, PyTorch/TensorFlow (SAS is an advantage),
- 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,
- Google Cloud Platform (Vertex AI, Workbench),
- Strong experience with Azure DevOps (Git, Pipelines),
- Experience with end-to-end pipelines (data → model → deployment).
You'll get extra points for:
- Experience working in Agile/Scrum teams,
- Knowledgeable on AI (risk) governance.
Your responsibilities:
- 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.
Information about 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.
The role naming convention in the global ING job architecture will be "Data Scientist IV".
The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.
How we score this
Risk AI Data Scientist at ING scores 97 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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