Data Scientist
Confirmed on the employer's own hiring board on Nov 12, 2026. First seen by Alion on Nov 12, 2026. Fuku scores A on the Alion truth index.
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.