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.