Risk AI Data Scientist

ING Hubs Romania; Warszawa; On-site; PLN 145,000 - 246,000; Full time

Job summary

ING Hubs Poland is seeking a Risk AI Data Scientist to drive the integration of advanced AI in risk management, including credit risk modelling and model lifecycle governance. You will design and deploy production-ready AI solutions that align with regulatory and risk policies.

The role involves building end-to-end AI pipelines, working with unstructured data, and evaluating GenAI systems, with opportunities to influence policy, governance, and risk strategy across ING locations.

Qualifications

• Master's degree in mathematics, economics or equivalent.
• 7+ years of experience in risk management (risk modelling a plus).
• Advanced Python, SQL, PyTorch/TensorFlow; SAS is an advantage.

Responsibilities

• Develop custom models by fine-tuning open-weight models on GCP GPUs for risk management.
• Evaluate and monitor GenAI systems in risk context (hallucinations, quality, drifting).
• Architect Retrieval-Augmented Generation (RAG) systems for internal policies and docs.
• Work with large-scale unstructured data (documents, PDFs, OCR).
• Design and implement agentic AI workflows to support risk managers.
• Build NLP pipelines to extract signals from unstructured text for features.
• Write clean, modular Python code in Azure DevOps for deployable models.

Skills

7+ years risk management

Advanced Python

SQL

PyTorch/TensorFlow

HuggingFace Transformers

PEFT

LangChain / LlamaIndex

Vector stores (FAISS)

RAG pipelines

GCP Vertex AI

Azure DevOps

end-to-end pipelines

Education

Master's degree in mathematics or economics or equivalent

Tools

SAS

Git

Pipelines

Job description

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.