- Location
- Singapore, Singapore, Singapore · published
- Type
- Full-time
- Department
- Education
- Seniority
- Senior
- Source
- Workable
Description
About Hytech
Hytech is a leading management consulting firm headquartered in Australia and Singapore, specializing in digital transformation for fintech and financial services companies. The firm provides comprehensive consulting solutions, as well as middle- and back-office support, to empower clients with streamlined operations and cutting-edge strategies. 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
The primary mission is to drive the machine learning (ML) and deep learning (DL) research agenda for the client-classification engine and adjacent transaction risk control/anti-fraud scenarios across the trading chain. The role involves taking the best ideas to production—safely, quickly, and cost-effectively. You will own the end-to-end experiment loop, from hypothesis to backtest, calibration, and monitored rollout, ensuring that routing and control decisions improve broker P&L, stability, and abuse prevention.
What You’ll Do
Essential (Core) Responsibilities:
- Serve as the key owner for modeling research execution: translate business and user questions into measurable data solutions, collaborating 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.
- Address related challenges: work on 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 deploy models safely, monitor their health, and set up retraining or recalibration triggers.
- Communicate clearly: write concise design documents and experiment summaries that are actionable for both technical and business stakeholders.
Advanced (Plus) Skills:
- Deep learning and time-series: experience with neural network models (e.g., LSTM, GRU, Transformers) or advanced architectures for temporal/user sequence data.
- Representation learning: proficiency with embeddings, autoencoders, or other feature-learning approaches to enhance prediction or personalization.
- Working with imperfect data: adept at handling noisy, incomplete, or partially labeled data (e.g., semi-supervised or weak supervision).
- Experimentation at scale: hands-on experience with tuning, validation, and reproducibility on large datasets or distributed compute environments (e.g., Spark/Databricks, MLflow).
- Business/context awareness: ability to connect modeling choices to real-world metrics and user/business value.
Minimum Qualifications
- 5+ years of experience in Data Science, ML, or DL, with a proven track record of using data and models to solve practical business or user problems.
- Strong proficiency in Python and SQL, with the ability to write clean, 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).
- Solid understanding of experiment design fundamentals, including careful validation, fair metrics, and awareness of data pitfalls.
- Effective communication skills, with the ability 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.