Postdoctoral Researcher in Data Science & Machine Learning for Financial Fraud Detection

Euraxess; Orléans; Hybrid; EUR 32,000 - 42,000; Full time

Job summary

UNIVERSITE D'ORLEANS invites a postdoctoral researcher to join Laboratoire d'Économie d'Orléans (LEO) to develop machine learning and deep learning models for fraud detection across insurance, social contributions, and banking. You will work with a multidisciplinary team and publish research in top journals.

The role requires a PhD in Data Science, ML, econometrics, finance, or statistics, strong English, and proficiency in Python or R.

Qualifications

• PhD in data science, ML, econometrics, finance or statistics.
• Strong expertise in machine learning and deep learning (ML/DL).
• Proficient programming in Python or R; experience with large-scale datasets and feature engineering.
• Familiarity with explainable AI (XAI) methods.

Responsibilities

• Develop ML/DL models for fraud detection in insurance, social contributions and banking.
• Conduct comparative studies of detection methodologies and preprocess large datasets.
• Publish 2–3 papers in peer-reviewed journals and present at major conferences.

Skills

Python

R

ML/DL

XAI

Education

PhD in Data Science/ML/Econometrics/Finance/Statistics

Tools

High-performance computing

Job description

Organisation/Company UNIVERSITE D'ORLEANS Research Field Mathematics » Statistics Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 12 Nov 2026 - 00:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 37H30 Offer Starting Date 4 Jan 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Presentation of the project:

Financial fraud generates major costs and evolves constantly with digitalisation and new payment channels. This project develops interpretable and operationally relevant methods to detect fraud in insurance, social contributions and banking.

The research uses large, heterogeneous and often sensitive datasets to identify anomalous behaviour, suspicious networks and emerging fraud patterns while controlling false positives and preserving model interpretability. Work will cover modelling under class imbalance, (semi-)supervised and cost-sensitive learning, network and graph methods to uncover coordinated schemes, and the development of explainable and robust models suited to regulated environments. Particular attention will be paid to adaptive strategies as fraud techniques evolve and to temporal dynamics.

Presentation of the Service / laboratory:

The postdoctoral researcher will join the Laboratoire d'Économie d'Orléans (LEO) and collaborate with academic and institutional partners. The environment supports quality research, with access to high-performance computing, proprietary fraud datasets, and a multidisciplinary team. The laboratory supports scientific publication and international conference participation. Attention will be paid to data confidentiality, responsible use and research reproducibility.

Presentation of the missions:

We seek a postdoctoral researcher to develop machine learning and deep learning models for fraud detection in insurance, social contributions and banking.
The work will include developing interpretable ML/DL methods for anomaly detection, analysing and preprocessing large datasets from diverse financial sectors, conducting comparative studies of detection methodologies, and collaborating with industry partners to validate models on proprietary data. The researcher will contribute to 2-3 publications in peer-reviewed international journals and present at major conferences. Scientific autonomy will be significant, with mentoring from senior researchers and access to laboratory infrastructure.

Présentation of the profile:

We are seeking a candidate who holds a Ph.D. in Data Science, Machine Learning, Econometrics, Finance, Statistics, or a related field, or who is expected to defend their doctoral thesis in the near future.
From a technical perspective, advanced expertise in machine learning and deep learning (ML/DL) is expected. The candidate should be proficient in programming with Python, R, or equivalent tools, have experience working with large-scale datasets and feature engineering, and be familiar with explainable AI (XAI) methods. Experience with network or graph analysis would be an asset.
From a professional perspective, the candidate should be able to conduct independent research, demonstrate excellent written and spoken English, work effectively in a collaborative environment, and handle confidential data appropriately. A strong interest in fraud detection or financial risk analysis is important. Previous experience working with insurance, banking, or social protection data, as well as proficiency in French, would be considered an asset.