Cybersecurity Financial Fraud Detection (AI ML) - Freelance

Job Title: Freelance Researcher – AI & Machine Learning for Adversarial Financial Fraud Detection Job Purpose: · Lead end-to-end research, design, and implementation of advanced AI/ML-based financial fraud detection systems under adversarial data poisoning scenarios. · Replicate, evaluate, and extend state-of-the-art Transformer-based fraud detection models using real-world financial transaction datasets. · Design and simulate controlled data poisoning attacks (label flipping, feature manipulation, subpopulation poisoning, and backdoor attacks) to assess system vulnerabilities. · Develop a robust Transformer-based detection model integrated with a Reinforcement Learning (RL) adaptive defence layer to counter evolving adversarial threats. · Deliver a complete, reproducible Ph.D.-level research output including implementation, experimental evaluation, and a Scopus-ready dissertation and publications. Required Qualification: · Master’s or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Cybersecurity, or related disciplines. · Strong academic or applied research background in fraud detection, adversarial machine learning, or cybersecurity analytics. · Demonstrated experience with deep learning architectures (especially Transformers) and reinforcement learning systems. Tools to be Familiar: · Programming: Python · ML/DL Frameworks: TensorFlow, PyTorch · Data Handling: NumPy, Pandas, Scikit-learn, CSV-based large-scale datasets · Visualization & Analysis: Matplotlib, Seaborn · Experimentation: Jupyter Notebook, reproducible ML pipelines Required Experience · Minimum 2–4 years of experience in applied machine learning, fraud detection, or adversarial ML research. · Prior experience working with financial transaction datasets or tabular ML problems. · Hands-on experience in replicating published research models and conducting comparative evaluations. Required Knowledge/Skills · Deep understanding of supervised learning, deep learning, and transformer architectures for tabular data. · Strong knowledge of data poisoning attacks, adversarial ML threats, and defence strategies. · Practical expertise in reinforcement learning formulation, reward engineering, and adaptive decision systems. · Ability to conduct statistically sound evaluations, robustness testing, and sensitivity analysis. · Strong analytical thinking, independent research capability, and meticulous documentation skills. · Excellent communication and collaboration skills for working with supervisors and reviewers. Job Description · Collect, preprocess, and engineer features from a large-scale real-world financial fraud dataset (IEEE-CIS Fraud Detection dataset). · Replicate and validate state-of-the-art fraud detection models from recent academic literature. · Design and implement controlled adversarial data poisoning scenarios applied strictly to training data. · Develop a Transformer-based fraud detection model robust to adversarial manipulation. · Integrate a Reinforcement Learning–based adaptive defence layer to dynamically respond to poisoning patterns and concept drift. · Design an ensemble defence framework combining Transformer predictions, RL decisions, and SOTA model confidence scores. · Conduct comprehensive experimental evaluation under clean and poisoned data conditions using standard fraud detection metrics. · Prepare complete, reproducible deliverables including source code, datasets, experimental logs, and documentation. Contact person: Gray-95661 33822 Job Types: Part-time, Freelance, Volunteer Contract length: 1 month Pay: From ₹10,000.00 per month Work Location: Remote