Junior Quantitative Analyst

at  TECHNOLOGY SERVICES GROUP PTE LTD

Our company is at the forefront of innovation in financial technology. Our mission is to develop an AI-driven platform that predicts market trends, empowering retail investors with the tools previously only accessible to professionals. We’re looking for a talented Senior Machine Learning Developer with an interest in finance to join our growing team in our Singapore office.

Job Responsibilities:

• Focuses on bridging financial models to machine learning and deep learning models to financial models.
• Focuses on bridging manual subject matter experts’ rule-based financial strategies to ML/AI strategical agents such as multi-agent modelling, macro-micro model ensembles.
• Combine statistical and non-statistical models via meta-learners.
• Range of work scope may involve that extends but not limited to: AI Stock Screener using Bayesian Modelling etc., AI Portfolio Optimization using Reinforcement Learning, AI Stock Trends Prediction using PyTorch Forecasting etc.
• Combine classical model evaluation with financial trading evaluation metrics such as Sharpe, Turnover, Fitness, Returns, Drawdown, Margin, Long Count, Short Count, Sector/Subindustry Allocations etc.
• Part of a sub-team focusing on full autonomous AI Trading under a larger trading division.

Job Requirements:

• Bachelor’s degree of Financial Engineering Background or relevant qualifications.
• Possess stack: PyTorch/Tensorflow, Python, R and SQL.
• Experiences in Alpha generations and metrics evaluation contextualization to financial trading metrics.
• Able to explain the meaning behind ‘everything is AI but nothing is AI’.

Kindly inform that only shortlisted candidate will be notified

• Focuses on bridging financial models to machine learning and deep learning models to financial models.
• Focuses on bridging manual subject matter experts’ rule-based financial strategies to ML/AI strategical agents such as multi-agent modelling, macro-micro model ensembles.
• Combine statistical and non-statistical models via meta-learners.
• Range of work scope may involve that extends but not limited to: AI Stock Screener using Bayesian Modelling etc., AI Portfolio Optimization using Reinforcement Learning, AI Stock Trends Prediction using PyTorch Forecasting etc.
• Combine classical model evaluation with financial trading evaluation metrics such as Sharpe, Turnover, Fitness, Returns, Drawdown, Margin, Long Count, Short Count, Sector/Subindustry Allocations etc.
• Part of a sub-team focusing on full autonomous AI Trading under a larger trading division