About This Role
5 years of experience
Skills: Python, Machine Learning, NLP, BERT, RoBERTa, FinBERT, Transformers, Hugging Face, PyTorch, TensorFlow, scikit-learn, spaCy, NLTK, Text Classification, Model Benchmarking, Precision Recall, F1 Score, AUC ROC, Threshold Tuning, Model Calibration, Drift Detection, PSI, KL Divergence, SHAP, LIME, Explainability, Feature Engineering, Tokenization, Data Processing, Pandas, NumPy, Spark, Dask, Financial Compliance, Market Abuse Detection, Insider Trading, Conduct Risk, Surveillance Systems
Role Overview
We are seeking an ML Developer with expertise in natural language processing and financial compliance to design, implement, and benchmark deterministic and transformer-based detection models for electronic communication surveillance. requires detect market abuse, conduct risk, information barrier breaches, and off-channel evasion across trader communications.
ASIC INFO 283 explicitly warns against reliance on vendor default alert thresholds, requiring licensees to calibrate models to their specific risk profile. You will build the model benchmarking framework that continuously tests and measures detection model effectiveness, enabling to demonstrate to regulators that its surveillance models are tuned, validated, and performing to measurable standards.
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
Understanding of financial services compliance: market abuse, insider trading, front-running, conduct risk
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or Computational Linguistics
Preferred Qualifications
Experience with surveillance platforms (NICE Actimize, Behavox, Shield FC) and their detection model architectures
Knowledge of ASIC INFO 283 model calibration requirements
Experience with voice analytics: speech-to-text, speaker diarisation, tonality/sentiment analysis
Familiarity with active learning and human-in-the-loop ML workflows
PhD in NLP, Computational Linguistics, or Machine Learning
Technical Skills & Tools
ML frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, spaCy, NLTK
NLP: BERT, RoBERTa, FinBERT, GPT-family, Word2Vec, FastText, sentence-transformers
Data processing: pandas, NumPy, Apache Spark, Dask, polars
MLOps: MLflow, Kubeflow, Azure ML, Weights & Biases, DVC
Model evaluation: SHAP, LIME, AUC-ROC, precision-recall curves, PSI, KL divergence
Infrastructure: Docker, Kubernetes, GPU compute (NVIDIA A100/H100), Azure ML Compute
Databases: PostgreSQL, Elasticsearch, vector databases (Pinecone, Weaviate, pgvector)
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