Portfolio Engineering - Internship

Capital Fund Management (CFM); Paris; On-site; EUR 13,000 - 20,000; Full time

Job summary

Capital Fund Management in Paris seeks an intern for the Portfolio Engineering - Directional team. You will observe and analyze constraint bindings within the internal optimization stack and help turn research signals into invested positions.

The role focuses on evaluating constraint formulations, proposing improvements, and validating changes against historical portfolios. You will work with Research and IT, using Python, cvxpy, and Git to deliver robust, interpretable results.

Qualifications

• Final year of an engineering school or MSc in a quantitative field.
• Strong background in convex optimization; cvxpy is a plus.
• Proficient in Python (pandas, NumPy) with clean, tested code.
• Comfort with real production data and ambiguity in findings.
• Interest in portfolio construction and financial markets; duality knowledge a plus.
• Follows good team practices: Git daily, code review, clear writing.

Responsibilities

• Instrument the optimisation process to observe constraints and their portfolio impact over historical data.
• Analyze constraint interactions and quantify effects on positions, turnover, risk and performance.
• Design alternative constraint formulations and evaluate against current ones using historical portfolios.
• Challenge existing modelling choices and propose evolutions or improvements.
• Formulate recommendations on constraint modelling evolution.
• Refactor constraint modelling, build dashboards and reusable tooling for quality and observability.
• Validate changes against real historical portfolios (inputs, active constraints, outputs).

Skills

Convex optimization

Python

CVXPY

Portfolio construction

Git

Data analysis

Education

Engineering school (final year)

MSc in applied mathematics / OR / data science / CS / finance

Tools

cvxpy

Git

Job description

Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients.

We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.

YOUR ROLE

The Portfolio Engineering - Directional team, part of the Directional Portfolio Analytics group, plays a central and transversal role within CFM, driven by the values of integrity and excellence. It designs and maintains the internal optimization stack that turns research signals into invested positions. Portfolios are built by solving optimisation problems under many constraints: position, trading, risk, margin and regulatory.

Constraints are meant to shape portfolios in specific ways, but today there is no systematic understanding of which constraints bind and when, how they interact, or whether current formulations produce the intended behavior. When a portfolio behaves unexpectedly, it is hard to tell whether the cause is the signal, the constraints, or their interaction. This is the core of the internship.

KEY RESPONSIBILITIES

• Instrumenting the optimisation process to observe which constraints bind, when, and with what portfolio consequences over historical data.
• Analysing constraint interactions and quantifying their impact on realised positions, turnover, risk and performance.
• Designing one or two alternative constraint formulations, potentially more interpretable or more robust, and evaluating them against the current ones on real historical portfolios.
• Challenging existing choices: identifying formulations that may be historical artefacts rather than considered design, without having to defend them.
• Formulating a recommendation on whether, and how, the current constraint modelling should evolve. "The current formulations are fine" is as valid an outcome as "improvements exist".

Continuously improving the stack:

• Refactoring constraint modelling: organisation, typing, tests and performance.
• Building monitoring dashboards and reusable tooling to improve the quality and observability of portfolio construction.
• Validating each change against real historical portfolios (inputs, active constraints, outputs).

YOUR SKILLS

• You are in the final year of an engineering school or an MSc in applied mathematics, operations research, data science, computer science, or finance.
• You have a solid background in convex optimisation; familiarity with cvxpy is a plus.
• You are strong in Python (pandas, NumPy) and write clear, tested, maintainable code.
• You are comfortable with real production data and with messy, inconclusive findings.
• You have a good grasp of, or a strong desire to learn, portfolio construction, and financial markets. Knowledge of duality and sensitivity analysis (shadow prices, Lagrange multipliers) is a plus.
• You follow good team practices: Git daily, code review, clear writing, direct exchanges with Research and IT.

EQUAL OPPORTUNITIES STATEMENT

We are continuously striving to be an equal opportunity employer and we prohibit any discrimination based on sex, disability, origin, sexual orientation, gender identity, age, race, or religion. We believe that our diversity, breadth of experience, and multiple points of view are among the leading factors in our success.

CFM is a signatory of the Women Empowerment Principles.

FOLLOW US

Follow us on Twitter or LinkedIn or visit our website to find out more about CFM.