About the role
About Hytech Hytech is a leading management consulting firm headquartered in Australia and Singapore, specializing in digital transformation for fintech and financial services companies. We provide comprehensive consulting solutions, as well as middle- and back-office support, to empower our clients with streamlined operations and cutting edge strategies. Our key clients include top trading platforms and cryptocurrency exchanges. With a global team of over 2,000 professionals, Hytech has established a strong presence worldwide, with offices in Australia, Singapore, Malaysia, Taiwan, Philippines, Thailand, Morocco, Cyprus, and more.
The mission Drive the ML/DL research agenda for our client-classification engine and adjacent transaction risk control/anti-fraud scenarios across the trading chain. Take the best ideas to production—safely, fast, and cost-effectively. You’ll own the end-to-end experiment loop, from hypothesis → backtest → calibration → monitored rollout, so our routing and control decisions improve broker P&L, stability, and abuse prevention.
What You’ll Do Essential (Core) • Be the key owner for modeling research execution: turn business and user questions into measurable data solutions; collaborate with analysts and engineers to build, test, and iterate. • Explore and apply ML/DL models: experiment with a variety of algorithms to solve practical integrity and classification problems. • Tackle related challenges: work on areas like user behavior modeling, fraud/anomaly detection, reputation/risk scoring, or abuse prevention, depending on business needs. • Own the experiment loop: design fair, time-aware evaluation strategies; conduct robust backtesting and validation; clearly report findings and next steps. • Partner for production: collaborate with engineers to ship models safely, monitor health, and set up retraining or recalibration triggers. • Communicate clearly: write concise design docs and experiment summaries that both technical and business stakeholders can act on. Advanced (Plus) • Deep learning and time-series: comfortable exploring neural network models (e.g., LSTM, GRU, Transformers) or advanced architectures for temporal/user sequence data. • Representation learning: experience with embeddings, autoencoders, or other feature-learning approaches to improve prediction or personalization. • Working with imperfect data: familiar with handling noisy, incomplete, or partially labeled data (e.g., semi-supervised or weak supervision). • Experimentation at scale: hands-on with tuning, validation, and reproducibility on large datasets or distributed compute (e.g., Spark/Databricks, MLflow). • Business/context awareness: able to connect modeling choices to real-world metrics and user/business value. Minimum qualifications • 5+ years in Data Science/ML/DL, with a track record of using data and models to solve practical business or user problems. • Strong Python and SQL; able to write clean, reproducible analysis code in notebooks or scripts. • Hands-on with at least one ML or DL library (scikit-learn, PyTorch, TensorFlow). • Solid experiment design fundamentals: careful validation, fair metrics, and an eye for avoiding data pitfalls. • Effective communicator: able to summarize findings and decisions for both technical and non-technical partners. Nice to have • Experience in integrity, fraud, abuse, personalization, recommendations, search, ads, or growth analytics. • Familiarity with cloud analytics stacks (Spark/Databricks/MLflow) or large-scale experimentation. • Exposure to classification, anomaly detection, or user segmentation in any domain.
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