A NY-based alternative investment firm is seeking an AI/Machine Learning Engineer to join their firm that is in process of implementing AI in numerous areas. using AI and ML techniques to support their trading strategies and business functions across a diverse investment platform. This role offers a unique opportunity to work within finance and technology, collaborating with Portfolio Managers, Quantitative Researchers and Traders.
Responsibilities
- Develop and implement AI and machine learning models to analyze financial data, generate predictive insights that support trading strategies and transform the firm's business processes
- Perform in-depth data analysis, utilizing large datasets to identify trends, patterns, and anomalies that can inform investment decisions.
- Conduct research to develop and refine models, leveraging the latest advancements in AI/ML methodologies.
- Design and test models and algorithms to support trading strategies, ensuring they are robust, efficient, and scalable.
- Work closely with Portfolio Managers, Researchers, the Risk team and other business functions to integrate AI/ML models into the firm's trading systems, strategies and business processes.
- Document research findings, model development processes, and performance metrics. Communicate results to both technical and non-technical stakeholders.
Qualifications
- PhD ideally in AI/ML, but could be in STEM with a focus in AI/ML
- Prior experience in finance is a plus
- Proficiency in programming languages commonly used in AI/ML and quantitative finance, such as Python
- Strong knowledge of AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and experience applying AI/ML techniques to real-world data.
- Excellent quantitative skills with the ability to develop and interpret complex models and algorithms.
- Experience with data manipulation, cleansing, and preprocessing techniques.
- Strong analytical and problem-solving skills, with a creative approach to developing innovative solutions.
- Ability to clearly communicate complex technical concepts to a diverse audience, including both technical and non-technical stakeholders.
