Total 3 researcher job vacancies in Engineering category in d21 upper bukit timah clementi park ulu pandan

Quantitative Trading Engineer / Quantitative Researcher

Working day: Monday to Friday, 9am to 6.30pm

Working location: Raffles Place

Salary: Discuss during interview

Responsibilities

Strategy Development:

• Use AI technologies such as machine learning and deep learning to intelligently optimize investment strategies and improve the performance of strategies in actual transactions.
• Extract predictive signals from financial data through both traditional statistical analysis methods and cutting-edge machine learning techniques.
• Formulate and apply mathematical modeling techniques to enhance existing trading strategies and perform innovative new research with the goal of identifying and capturing trading opportunities.

Data Analysis：

• Collect and analyze large financial datasets (market data, news, social media, etc.).
• Work with large data sets to predict and test statistical market patterns, combining knowledge of systems, mathematical techniques, and trading to identify the best places to improve the trading system.
• Build and optimize feature engineering pipelines to improve model prediction accuracy.

Financial Modeling:

• Construct, back-test, maintain, and improve financial models to support trading activities.
• Conceptualize valuation strategies, develop, and continuously improve upon mathematical models and help translate algorithms into code
• Conduct research and statistical analysis to build and refine monetization systems for trading signals
• Optimize the order execution and risk management of our trading system
• Create robust solutions to problems presented in the trading environment
• Build regression, classification, reinforcement learning and other models for market forecasting and trading decisions.
• Tuning the model's hyperparameters to improve model performance and stability.

Strategy backtesting and real-time testing:

• Backtest historical data to evaluate the strategy's yield, volatility, drawdown and other indicators.
• Deploy the strategy to the real-time environment, track performance and continuously optimize.

Requirements

• Master’s degree or above in Statistics, Mathematics, Applied Mathematics, Computer Science, Financial Engineering or equivalent.
• At least 2 years of experience in quantitative trading and machine learning modeling.
• Proficient in programming languages ​​such as Python, C++, Java, etc.
• Familiar with common machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Familiar with quantitative trading systems, data flows and algorithms.
• Deep understand the structure of financial markets and be familiar with markets such as virtual currencies, stocks, futures, and foreign exchange.
• Experience in large-scale data processing, proficient in using SQL, NoSQL, Hadoop and other tools.

Bonus Skills

• Experience in developing high-frequency trading strategies will be an advantage.
• Participated in AI competitions (such as Kaggle) or published papers in top conferences (such as NeurIPS, ICML) will be an advantage.
• Familiar with cutting-edge AI technologies such as reinforcement learning, and generative adversarial networks (GANs) will be an advantage.

Quantitative Trading Engineer / Quantitative Researcher

Working day: Monday to Friday, 9am to 6.30pm

Working location: Raffles Place

Salary: Discuss during interview

Responsibilities

Strategy Development:

• Use AI technologies such as machine learning and deep learning to intelligently optimize investment strategies and improve the performance of strategies in actual transactions.
• Extract predictive signals from financial data through both traditional statistical analysis methods and cutting-edge machine learning techniques.
• Formulate and apply mathematical modeling techniques to enhance existing trading strategies and perform innovative new research with the goal of identifying and capturing trading opportunities.

Data Analysis：

• Collect and analyze large financial datasets (market data, news, social media, etc.).
• Work with large data sets to predict and test statistical market patterns, combining knowledge of systems, mathematical techniques, and trading to identify the best places to improve the trading system.
• Build and optimize feature engineering pipelines to improve model prediction accuracy.

Financial Modeling:

• Construct, back-test, maintain, and improve financial models to support trading activities.
• Conceptualize valuation strategies, develop, and continuously improve upon mathematical models and help translate algorithms into code
• Conduct research and statistical analysis to build and refine monetization systems for trading signals
• Optimize the order execution and risk management of our trading system
• Create robust solutions to problems presented in the trading environment
• Build regression, classification, reinforcement learning and other models for market forecasting and trading decisions.
• Tuning the model's hyperparameters to improve model performance and stability.

Strategy backtesting and real-time testing:

• Backtest historical data to evaluate the strategy's yield, volatility, drawdown and other indicators.
• Deploy the strategy to the real-time environment, track performance and continuously optimize.

Requirements

• Master’s degree or above in Statistics, Mathematics, Applied Mathematics, Computer Science, Financial Engineering or equivalent.
• At least 2 years of experience in quantitative trading and machine learning modeling.
• Proficient in programming languages ​​such as Python, C++, Java, etc.
• Familiar with common machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Familiar with quantitative trading systems, data flows and algorithms.
• Deep understand the structure of financial markets and be familiar with markets such as virtual currencies, stocks, futures, and foreign exchange.
• Experience in large-scale data processing, proficient in using SQL, NoSQL, Hadoop and other tools.

Bonus Skills

• Experience in developing high-frequency trading strategies will be an advantage.
• Participated in AI competitions (such as Kaggle) or published papers in top conferences (such as NeurIPS, ICML) will be an advantage.
• Familiar with cutting-edge AI technologies such as reinforcement learning, and generative adversarial networks (GANs) will be an advantage.

Quantitative Trading Engineer / Quantitative Researcher

Working day: Monday to Friday, 9am to 6.30pm

Working location: Raffles Place

Salary: Discuss during interview

Responsibilities

Strategy Development:

• Use AI technologies such as machine learning and deep learning to intelligently optimize investment strategies and improve the performance of strategies in actual transactions.
• Extract predictive signals from financial data through both traditional statistical analysis methods and cutting-edge machine learning techniques.
• Formulate and apply mathematical modeling techniques to enhance existing trading strategies and perform innovative new research with the goal of identifying and capturing trading opportunities.

Data Analysis：

• Collect and analyze large financial datasets (market data, news, social media, etc.).
• Work with large data sets to predict and test statistical market patterns, combining knowledge of systems, mathematical techniques, and trading to identify the best places to improve the trading system.
• Build and optimize feature engineering pipelines to improve model prediction accuracy.

Financial Modeling:

• Construct, back-test, maintain, and improve financial models to support trading activities.
• Conceptualize valuation strategies, develop, and continuously improve upon mathematical models and help translate algorithms into code
• Conduct research and statistical analysis to build and refine monetization systems for trading signals
• Optimize the order execution and risk management of our trading system
• Create robust solutions to problems presented in the trading environment
• Build regression, classification, reinforcement learning and other models for market forecasting and trading decisions.
• Tuning the model's hyperparameters to improve model performance and stability.

Strategy backtesting and real-time testing:

• Backtest historical data to evaluate the strategy's yield, volatility, drawdown and other indicators.
• Deploy the strategy to the real-time environment, track performance and continuously optimize.

Requirements

• Master’s degree or above in Statistics, Mathematics, Applied Mathematics, Computer Science, Financial Engineering or equivalent.
• At least 2 years of experience in quantitative trading and machine learning modeling.
• Proficient in programming languages ​​such as Python, C++, Java, etc.
• Familiar with common machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Familiar with quantitative trading systems, data flows and algorithms.
• Deep understand the structure of financial markets and be familiar with markets such as virtual currencies, stocks, futures, and foreign exchange.
• Experience in large-scale data processing, proficient in using SQL, NoSQL, Hadoop and other tools.

Bonus Skills

• Experience in developing high-frequency trading strategies will be an advantage.
• Participated in AI competitions (such as Kaggle) or published papers in top conferences (such as NeurIPS, ICML) will be an advantage.
• Familiar with cutting-edge AI technologies such as reinforcement learning, and generative adversarial networks (GANs) will be an advantage.