Data Scientist

Hong KongFull-timemid

Data ScientistData

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Overview

A fast-growing technology-driven firm in the financial intelligence space is looking for a Data Scientist to join its high-performing analytics team.

Technical Tools

numpypandaspythonpytorchscikit-learnsqltensorflowforecastingmachine-learningquantitative-analysis

A fast-growing technology-driven firm in the financial intelligence space is looking for a Data Scientist to join its high-performing analytics team. This role is ideal for someone passionate about applying machine learning, statistics, and quantitative research techniques to complex financial and alternative datasets.

You will work on developing predictive models, extracting meaningful signals from large-scale data, and supporting data-driven decision making in a highly analytical environment. The position offers strong exposure to financial markets, real-time data systems, and advanced AI/ML applications.

Develop and deploy machine learning and statistical models for forecasting, pattern recognition, and financial analytics.

Work with structured and unstructured datasets including market data, alternative datasets, and event-driven information sources.

Design and maintain scalable data preparation and feature engineering workflows.

Conduct rigorous backtesting, validation, and performance analysis to evaluate model robustness and reduce overfitting risk.

Identify proprietary indicators and predictive features that improve analytical accuracy and signal quality.

Collaborate closely with cross-functional stakeholders to translate technical findings into commercially meaningful insights.

Continuously evaluate data integrity, model assumptions, and research methodologies to maintain high analytical standards.

Bachelor’s or Master’s degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Physics, Financial Engineering, or a related field.

2+ years of experience working with large-scale analytical or financial datasets.

Strong foundation in statistics, probability, machine learning, and quantitative analysis.

Familiarity with financial markets, trading concepts, or investment data is advantageous.

Advanced proficiency in Python, including common data science and machine learning libraries (e.g. Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow).

Strong SQL and data querying capabilities.

Experience handling high-volume or real-time datasets is a plus.

Strong critical thinking skills with a detail-oriented and hypothesis-driven approach to problem solving.