PhD “Considering Temporality in Dynamic Socio-Transactional Networks” M/F

Job title:

PhD “Considering Temporality in Dynamic Socio-Transactional Networks” M/F

Company:

Orange

Job description

about the roleYour role is to conduct a PhD thesis on: “Considering Temporality in Dynamic Socio-Transactional Networks.”

• Global Context and Problematic of the Subject

Orange Money is a financial solution offered in 17 countries across the African continent, experiencing significant growth in terms of user numbers, transaction volumes, and services provided.The concept of social networks refers to the entirety of relationships between individuals or social groups. These relationships can form in various social circles such as family, neighborhood, leisure, friendship, or professional environments. It is therefore possible to establish the social network of users of a financial service. Furthermore, financial transactions contain temporal information that can be useful. The question then arises as to how to analyze this data to enhance the service offered.Social network analysis is based on mathematical tools such as network theory and graph theory. Research has particularly aimed to identify specific individuals within the network or to segment the network into communities of users. Recent research has increasingly focused on graph machine learning, which involves machine learning methods operating on graph/network data types. The most recent studies have also incorporated temporal information to make predictions on data represented in graph form.

• Scientific Objective – Results and Challenges to Overcome

The main objective of this PhD thesis is to explore whether considering the temporal aspect allows for predicting the evolution of interest groups within socio-transactional graphs. This research aims to establish robust predictive models that integrate the temporal dynamics of social and transactional interactions.The main challenges include validating recent predictive models, such as GraphCAST, specifically on socio-transactional networks to ensure their effectiveness. It is also necessary to determine how to combine graph-based predictive models, integrating nodes and edges, into relevant predictive features for interest groups. Additionally, the issue of the explainability of predictive models must be addressed to ensure transparency and understanding of the results.The data used for constructing these graphs will come from Orange Money transaction data. It is planned to enrich these networks with additional data such as call detail records (CDR). The associated technical challenges concern the processing of large amounts of data in a reasonable time frame.about you

• Required Skills (Scientific and Technical) and Personal Qualities for the Position

You have skills in software development, algorithms, and mathematics. Skills in graph theory, data science, machine learning, or distributed computing (Spark) would be a plus.You are capable of synthesizing research articles.You can read, write, and speak in English.You have a good team spirit.

• Required Education

You hold a Master’s degree (Master 2) or an engineering degree in computer science or data science.

• Desired Experience

You have completed an internship or apprenticeship during which you gained experience in software development, preferably using graphs and/or time series.Ideally, you have some experience in Machine Learning and/or Artificial Intelligence.A preliminary research-oriented experience would be a plus.additional information

• What Adds Value to This Offer?

Digital financial services generate significant volumes of usage data, and today, the use of machine learning already allows us to enhance and secure our services or improve our customer knowledge. However, these methods do not sufficiently leverage the knowledge related to the structure and temporality of the social network formed by Orange Money transactions.Working in an innovative environment within an entity at the forefront of mobile financial services, along with collaboration with subsidiaries, should enable the operational entities to offer graph-oriented social indicators and software solutions that improve the operational efficiency of Orange Group’s financial services and contribute to providing innovative, high-performance features to our 70 million customers.departmentOrange Innovation brings together the research and innovation activities and expertise of the Group’s entities and countries. We work every day to ensure that Orange is recognized as an innovative operator by its customers and we create value for the Group and the Brand in each of our projects. With 720 researchers, thousands of marketers, developers, designers and data analysts, it is the expertise of our 6,000 employees that fuels this ambition every day.Orange Innovation anticipates technological breakthroughs and supports the Group’s countries and entities in making the best technological choices to meet the needs of our consumer and business customers.Within Innovation, you will be integrated into a research team working on Orange Money, the money transfer and mobile payment service of the Orange Group, offered in 17 African countries. Orange Money, a growth driver for the Orange Group, allows its users to deposit money into an account linked to their mobile number to access a range of services, including domestic and international money transfers, bill payments, and mobile credit purchases.contractThesis

Expected salary

Location

Caen, Calvados

Job date

Wed, 26 Mar 2025 23:07:04 GMT

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