Risk AI Data Scientist
Warszawa, Polska
Najważniejsze cechy oferty
Min. 5 lat doświadczenia
DevOps / Cloud: AWS, Azure, Docker, Kubernetes
Data: SQL / BI / Python
Pełny etat
Praca na miejscu - bez trybu zdalnego
Description
ING Hubs Poland is hiring!
Nr ref.
REQ-10118060
Spółka
ING Hubs
Kategoria
Data Science
Lokalizacja
Warsaw, Poland
Data publikacji
10/07/2026
Poziom stanowiska
Professional
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.
You'll get extra points for
Experience working in Agile/Scrum teams,
Knowledgeable on AI (risk) governance.
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).