Data Scientist, Risk & Quant Analytics

On-siteMidData Scientistposted 3mo ago

✦Role summaryAI-generated

Polymer Capital Hong Kong seeks a Data Scientist to analyze market, position, and alternative data to uncover trading signals and enhance risk management. The role involves building and validating predictive models, performing hypothesis testing, and translating insights into actionable trading strategies.

Skills required

Python Machine learning Statistical modeling Hypothesis testing Data cleaning Predictive modeling Market data analysis Sentiment analysis Big data Data analysis AI Data science Statistics R pandas NumPy scikit-learn TensorFlow

About this role

We are seeking a skilled and motivated Data Scientist to join our team, focusing on analyzing data to uncover actionable insights and patterns. The ideal candidate will leverage statistical methods, machine learning techniques, and domain knowledge to discover signals that will drive trading strategies and enhance risk management decision-making.

Key Responsibilities

Data Analysis & Modeling: Collect, clean, and analyze vast datasets, including market data, PM position data, social media sentiment, alternative big data, and economic indicators, to uncover trading patterns, behavior patterns, and correlations.
Predictive Modeling: Develop and deploy machine learning, artificial intelligence, and statistical models to forecast market, industry sector, alpha, and security movements/rankings.
Testing & Evaluation: Rigorously hypothesis testing, evaluation, and refining models to ensure robustness of decision making.
Data Management: Handle the complex process of data preparation and management for model building and analysis.

Qualifications

Education: Advanced degree (Master’s or above) in Data Science, Statistics, Mathematics, Computer Science, or a related field.

Requirements

Deep understanding of statistical & probability analysis and quantitative methods. Strong problem-solving abilities, with a knack for deriving insights from complex datasets.
Proficiency in programming languages such as Python or R.
Experience with data manipulation libraries (e.g., pandas, NumPy)
Familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow).
Excellent verbal and written communication skills, with the ability to effectively present complex findings clearly to diverse audiences.
Ability to work collaboratively in a team-oriented environment and to foster a culture of learning and knowledge-sharing within the team.
A good understanding of financial markets, market microstructure, trading algorithms and/or the business context of data science applications is a strong plus
Research or experience related to behavioral finance is a strong plus