Investment Data Scientist | Financial Modeling & Portfolio Analytics
About The Position
As part of the AMS Research team, the Investment Data Scientist plays a key role in developing, improving, and evaluating quantitative models that directly support investment decision-making and portfolio management. This is a hands-on technical role focused on writing production-quality Python code to analyze financial datasets, enhance portfolio construction methods, and automate investment workflows. You’ll collaborate closely with Investment Committee members, analysts, and other stakeholders to improve the investment process through portfolio optimization, statistical modeling, and process automation. You’ll also develop interactive dashboards and visualizations that translate complex analytical outputs into actionable insights. This role requires strong analytical thinking, the ability to solve complex technical problems independently and collaboratively, and a commitment to delivering high-quality, validated results.
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.
- Translating business problems into quantitative models and technical solutions.
- Building and maintaining automated workflows.
- Creating clear, intuitive data visualizations for non-technical audiences.
- Validating and testing models to ensure accuracy and reliability.
- Performing performance calculations and financial data analysis.
- Work independently and collaboratively in a fast-paced team environment.
- Deliver accurate, high-quality analytical work through rigorous testing and validation.
- Manage multiple projects with competing deadlines.
- Communicate complex technical concepts clearly to non-technical stakeholders.
- Debug code, validate results, and ensure analytical accuracy.
- Balance attention to detail with strategic thinking.
- Adapt to changing priorities and requirements.
- Learn new quantitative techniques and investment concepts quickly.
- Promote team effectiveness through knowledge sharing and collaboration.
- 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.
- Demonstrated experience building optimization models, statistical systems, and/or automated workflows.
Nice To Haves
- Financial markets, investment products, and portfolio theory.
- Performance measurement and attribution methodologies.
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees
