Senior Data Scientist - ML/DL Research

About the role

Hytech, a management consulting firm based in Australia and Singapore, is looking for a Senior Data Scientist to lead its machine learning and deep learning research agenda. The focus is on a client-classification engine and adjacent transaction risk control and anti-fraud scenarios across the trading chain. The role owns the end-to-end experiment loop, from hypothesis through backtest, calibration and monitored rollout, so that routing and control decisions improve broker profit and loss, stability and abuse prevention.

Key responsibilities

• Translate business and user questions into measurable data solutions, working with analysts and engineers to build, test and iterate models.
• Explore and apply a range of ML/DL algorithms to solve practical integrity and classification problems.
• Work on user behavior modeling, fraud/anomaly detection, reputation/risk scoring or abuse prevention, depending on business needs.
• Design fair, time-aware evaluation strategies, run robust backtesting and validation, and report findings and next steps clearly.
• Partner with engineers to deploy models safely, monitor their health and set up retraining or recalibration triggers.
• Write concise design documents and experiment summaries understandable by technical and business stakeholders.

Required profile

• 5+ years of experience in data science, machine learning or deep learning, with a proven track record of solving practical business or user problems.
• Solid understanding of experiment design, including validation, fair metrics and awareness of data pitfalls.
• Effective communication skills, able to summarise findings for technical and non-technical partners.

Required skills

• Strong Python and SQL, with reproducible analysis code in notebooks or scripts.
• Hands-on experience with at least one ML or DL library such as scikit-learn, PyTorch or TensorFlow.
• Nice to have: deep learning and time-series models (LSTM, GRU, Transformers), representation learning (embeddings, autoencoders), handling noisy or partially labeled data, and large-scale experimentation with Spark/Databricks and MLflow.