AI/ML Quantitative Researcher
AI/ML Quantitative Researcher
AI/ML Quantitative Researcher
Our client is a leading quantitative trading team that is looking to capitalize on recent growth to expand their AI/ML research capabilities. The team is seeking a highly qualified AI/ML quantitative researcher to expand their existing capabilities by working with portfolio managers and traders on the research and development of AI tools. This is an excellent opportunity to interface with multiple trading desks across the business and generate impact through business critical tools and applications.
Responsibilities:
- Conduct quantitative research and analysis to develop and implement LLM focused AI/ML models for financial markets.
- Collaborate with cross-functional teams to integrate AI/ML models and tools into production systems.
- Analyze large datasets to identify patterns, trends, and insights that can inform decision-making.
- Stay up-to-date with the latest advancements in AI/ML and apply them to enhance our models and strategies.
- Communicate research findings and model performance to stakeholders across business lines.
Qualifications:
- 1+ years of experience in an AI/ML focused research role within quantitative finance.
- A master's or Ph.D. in a quantitative discipline such as Computer Science, Mathematics, Statistics, or Finance is preferred.
- Strong proficiency in programming languages such as Python or C++.
- Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Solid understanding of statistical analysis, probability theory, and financial mathematics.
- Familiarity with financial markets, trading strategies, and risk management principles.
- Excellent problem-solving skills and the ability to work independently and collaboratively.
- Strong communication skills to effectively convey complex technical concepts to non-technical stakeholders.
- A master's or Ph.D. in a quantitative discipline such as Computer Science, Mathematics, Statistics, or Finance is preferred.
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