As part of the AMS Research team at Raymond James, the Investment Data Scientist plays a key role in developing, improving, and evaluating quantitative models that directly support investment decision-making and portfolio management.
Requirements
- Python libraries for data manipulation and array mathematics: pandas, NumPy, SciPy, and optimization libraries such as CVXPY.
- Statistical modeling and optimization: mixed integer programming, regressions, time series, and Monte Carlo simulation.
- Data visualization: Streamlit, Tableau, Power BI, Plotly Dash, or similar platforms.
- Quantitative finance: portfolio construction methods, risk modeling, and financial data analysis.
- Writing clean, documented, and version-controlled Python code.
- Django framework for database management.
- Advanced investment concepts and practices in the securities industry.
Responsibilities
- Develop and maintain robust Python code for portfolio construction, statistical analysis, and automation of investment workflows.
- Design and implement portfolio optimization algorithms.
- Apply advanced statistical methods to extract insights from financial datasets.
- Collaborate with Investment Committee members, analysts, and quant team members to align model development with investment objectives and operational needs.
- Build automated processes to eliminate manual tasks, reduce errors, and improve workflow efficiency.
- Create interactive dashboards and visualizations to communicate analytical findings.
Other
- Bachelor’s degree in Computer Science, Mathematics, Statistics, Physics, Engineering, Economics, Finance, or a related quantitative field.
- 3–6 years of hands-on experience with Python development and quantitative analysis.
- Less than 25% travel required.
- Hybrid workstyle.
- Ability to work independently and collaboratively in a fast-paced team environment.
