About the job
Our quantitative strategists are at the cutting edge of our business and solve real-world problems through a variety of analytical methods. As a member of our team, you will utilize your training in mathematics, programming, and logical thinking to build quantitative models that drive success in our business. Your problem-solving talents and aptitude for innovation will help define your contributions and enable you to find solutions to a broad range of problems, in a dynamic, fast-paced environment.
Responsibilities
Developing and deploying ML models for fraud and anomaly detection as well as business workflows enhancement
Delivering risk metrics and quantitative analytics for financial and non-financial risks across wealth management
Develop AI-led solutions to improve efficiency and accuracy in risk management.
Building and maintaining robust and systematic risk management tools and reporting
Collaborating on the design of new and existing strategies to address clients’ investment goals.
Developing and maintaining risk management and portfolio analysis tools across multiple asset classes for senior management and portfolio managers.
Building and maintaining infrastructure of Strategists’ analytical systems.
Qualifications
Minimum
Bachelor, Masters or Ph.D. in a quantitative or engineering field, e.g. mathematics, physics, quantitative finance, computational finance, computer science, engineering
1-3 years of experience in the job offered or related quantitative financial modeling and software development positions
Programming and mathematical skills are required
Creativity, problem-solving skills, and ability to communicate complex ideas to a variety of audiences
A self-starter, should have ability to work independently as well as thrive in a team environment
Preferred
Excellent understanding of machine learning techniques and algorithms, such as gradient boosting decision trees, random forests, etc., is a plus
Experience with building models using common data science toolkits, i.e., Python (Pandas, NumPy, Scikit-learn) and Spark
Experience with prompt engineering, working with LLM models, and MCP.
Previous work experience in: Utilizing statistical methods, including time-series and regression analysis; programming in object-oriented languages for efficient model implementations; manipulating data sets using relational databases and SQL