Quantitative Data Scientist — Trading & Modelling

Capital Dynamics Asset Management Sdn Bhd; Kuala Lumpur; On-site; MYR 120,000 - 200,000; Full time

Benefits offered by this job

Medical

Performance-based pay

Job summary

Capital Dynamics Asset Management Sdn Bhd is seeking a data science professional in Kuala Lumpur to research market trends, build quantitative models, and support trading decisions. The role involves risk analytics, forecasting, and developing data algorithms to improve company products.

The ideal candidate will have 2+ years in data science, strong Python/R skills, and solid foundations in statistics, time-series analysis, and ML techniques.

Qualifications

• Bachelor's degree in Computer Science, Mathematics, Statistics or related computational discipline from a reputable university.
• Master's qualification would be an advantage.

Responsibilities

• Researching and analysing market trends using quantitative models and technical analysis to support trading decisions.
• Developing and implementing complex quantitative models (e.g. trading & forecasting).
• Performing daily statistical analyses (e.g. risk analytics and default risk modelling) and coding tasks (e.g. pattern recognition or machine learning).
• Detailing model specifications and data collection methods.
• Testing new models, products and analytics programs.
• Developing optimal strategies.
• Developing new data algorithms and models to support company products.
• Presenting and interpreting data results to senior management and clients.

Skills

Python

R

Statistics

Time-series analysis

Pattern recognition

Machine learning

SVM

K-Means

Decision Trees

Random Forest

Education

Bachelor's degree in Computer Science

Bachelor's degree in Mathematics or Statistics

Master's degree preferred

Job description

Capital Dynamics Asset Management Sdn Bhd is seeking a data science professional in Kuala Lumpur to research market trends, build quantitative models, and support trading decisions. The role involves risk analytics, forecasting, and developing data algorithms to improve company products.

The ideal candidate will have 2+ years in data science, strong Python/R skills, and solid foundations in statistics, time-series analysis, and ML techniques.