Location: Hanoi & Ho Chi Minh City, Vietnam
Position: Data Science Intern
Salary: VND 3.5 – 5 million/month
Experience Required: No prior experience required
Application Deadline: July 3, 2026
Working Schedule: Monday – Friday, 08:30 AM – 05:30 PM (Flexible according to academic schedule)

About the Internship

We are seeking highly motivated students and recent graduates who are passionate about Data Science, Machine Learning, and quantitative finance. This internship offers an excellent opportunity to gain hands-on experience working with real-world financial data, developing predictive models, and contributing to systematic trading research projects.

Interns will collaborate with experienced Data Scientists, Data Engineers, and Software Developers while learning industry best practices in machine learning and quantitative analysis.

Key Responsibilities

• Assist in researching, developing, and implementing predictive models for financial time-series analysis.
• Support the design and backtesting of systematic trading strategies using technical indicators, Machine Learning (ML), and Deep Learning (DL) techniques.
• Participate in feature engineering, hyperparameter tuning, and model evaluation to improve predictive performance.
• Assist in maintaining, updating, and deploying ML/DL pipelines for live trading environments.
• Collaborate with Data Engineers and Developers to integrate research models into production systems.
• Monitor trading strategy performance and contribute to model refinement based on market conditions and real-world results.

Qualifications and Requirements

Candidates should have a strong interest in Data Science and a solid foundation in mathematics and machine learning.

Required Knowledge and Skills

• Strong background in Mathematics, including:
  • Linear Algebra
  • Calculus
  • Probability Theory
  • Statistics
  • Optimization
• Good understanding of Machine Learning fundamentals:
  • Regression and Classification
  • Clustering
  • Data Preprocessing
  • Model Evaluation
  • Performance Optimization
• Familiarity with:
  • Ensemble Learning techniques (Bagging, Boosting)
  • Regularization methods (L1/L2)
• Basic understanding of Neural Networks and practical experience with PyTorch.
• Knowledge of common Deep Learning architectures:
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory Networks (LSTMs)
  • Convolutional Neural Networks (CNNs)
• Experience working with time-series data, including feature engineering and predictive modeling.
• Proficiency in Python and familiarity with major ML/DL frameworks such as:
  • Scikit-learn
  • TensorFlow
  • PyTorch

Preferred Qualifications

• Knowledge of portfolio optimization, risk management, or market microstructure.
• Experience with cloud deployment environments or real-time data processing systems.
• Participation in data science, machine learning, or quantitative competitions such as Kaggle, Numerai, or similar platforms.

Benefits

• Opportunity to work directly with real financial market data and quantitative trading models.
• Exposure to practical applications of Machine Learning and Deep Learning in finance.
• A young, dynamic, and supportive working environment.
• Mentorship from experienced professionals in Data Science and quantitative research.
• Internship allowance starting from VND 3 million/month, with performance-based increases during the internship period.
• Lunch allowance provided.
• Flexible working arrangements to accommodate academic schedules.
• Opportunities to participate in annual company trips and team-building activities.

Working Locations

Hanoi Office

N04B-T1 Lanmak Tower, Ngoai Giao Doan Area, Xuan Dinh Ward, Hanoi

Ho Chi Minh City Office

9th Floor, International Plaza Building, 343 Pham Ngu Lao Street, Ben Thanh Ward, Ho Chi Minh City

Ideal Candidates

This internship is particularly suitable for students majoring in:

• Data Science
• Artificial Intelligence
• Computer Science
• Information Technology
• Applied Mathematics
• Statistics
• Financial Engineering

Students who are passionate about Machine Learning, Deep Learning, Quantitative Finance, and Financial Data Analytics are strongly encouraged to apply.