# Data Scientist

> Fuku · Singapore, Singapore · Full-time · Posted 2026-11-12

**Workplace:** on_site

## 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. 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.

## Apply

[Apply at Fuku](https://apply.workable.com/fuku/j/857D6E1CF5/apply)

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