BNP Paribas Fortis is a leading bank based in Belgium that provides a comprehensive range of financial services to individual, corporate, and institutional clients. As part of the BNP Paribas Group, we benefit from the strength and stability of one of the world's largest banking networks. We offer a broad spectrum of products, including retail banking, wealth management, and investment solutions, while emphasizing digital innovation to enhance customer experience. We are committed to sustainable development and corporate social responsibility, aiming to support the economic growth and well-being of the communities we serve.
As part of our ongoing effort to modernize our software application landscape through application lifecycle management and cloud migration, we face significant challenges in constructing and valorizing a comprehensive knowledge base of our internal software. To this end,
we have invested resources in developing a graph-based approach to knowledge management, resulting in a sizable knowledge graph that describes the structure of our codebase, interactions of various services, etc. This creates a "Digital Twin" of our application environment.
Our goal is to exploit advanced graph-based methods (e.g., graph theory, graph learning/mining) to extract operationally and strategically valuable information from the knowledge graph.
We aim to modernize the application management lifecycle and application migration management. We seek to explore how the existing knowledge graph can be valorized to achieve these objectives by leveraging advanced methodologies in graph theory, graph mining, and graph learning that go beyond market standards.
Use cases:
1. Extracting insights from the knowledge graph to improve migration wave planning.
2. Using information encoded in the digital twin to facilitate the migration of legacy applications to the new infrastructure.
3. Fluent in Dutch or French.
4. Minimum of a Master’s degree in a quantitative discipline such as statistics, mathematics, or engineering (a PhD is an advantage).
5. Expectation: 50% on-site & 50% homeworking.
Experience :
Senior data science expert with expertise in digital twins and graph-based approaches, ensuring the use of state-of-the-art technology during the mission while supporting the team in their daily work.
Technical experience:
1. AI - Machine Learning: R/RStudio, SAS, LLMs, NLP, ASR, Graph learning.
2. Programming languages: Python, Spark, scripts (Ansible), SQL, Scala (optional), Kafka (optional), Flink (optional).
3. Development tools & libraries: Conda, GitLab, Jupyter, PyCharm.
4. Databases: SQL, Neo4j.
Join our team!
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