Description

Key Responsibilities

• Design and implement deterministic detection models for eComms surveillance: market abuse language, insider information patterns, tipping-off phraseology, information barrier breaches, conduct risk, off-channel evasion, and trade-comms correlation
• Develop and fine-tune transformer-based NLP models (BERT, RoBERTa, FinBERT) for context-aware detection beyond simple lexicon matching
• Build and maintain a model benchmarking framework with ground truth datasets, precision/recall/F1 measurement, AUC-ROC analysis, and automated weekly benchmark runs
• Implement threshold tuning workflows to calibrate alert sensitivity per desk, asset class, and jurisdiction — ensuring compliance with ASIC INFO 283 guidance
• Design false positive reduction strategies: contextual filtering, trader baseline profiling, alert clustering, and analyst feedback loops
• Develop false negative detection: red team simulated misconduct, historical replay testing, coverage gap analysis, and cross-model ensemble voting
• Prepare communication data for transformer models: tokenisation, sequence formatting with context windowing, label engineering from investigations, and data augmentation
• Implement model drift detection using PSI and KL divergence with automated alerts when distributions shift
• Build champion-challenger evaluation: shadow-mode deployment with statistical significance testing before production promotion
• Deliver model explainability using SHAP/LIME for regulatory audit readiness
• Produce monthly model effectiveness scorecards for compliance committee review
• Collaborate with the eComms pipeline team to ensure clean, normalised inputs for ML model training and inference

Required Qualifications

• 5+ years in machine learning engineering, with at least 3 years in NLP/text classification in financial services or compliance
• Strong proficiency in Python (scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers)
• Hands-on experience fine-tuning pre-trained language models (BERT, RoBERTa, GPT-family) for domain-specific tasks
• Experience building model evaluation and benchmarking pipelines with automated metric tracking and drift detection
• Understanding of financial services compliance: market abuse, insider trading, front-running, conduct risk
• Experience with threshold tuning, FP/FN trade-off analysis, and precision-recall optimisation
• Familiarity with model explainability frameworks (SHAP, LIME) and model governance requirements
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or Computational Linguistics

Key Skills

Education

Bachelor's or Master's degrees