Prediction Engineer
Paris, 75, FR
ABOUT CFM
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.
About IT-Prediction
• IT-Prediction is the engineering team responsible for predictor quality at scale. We ensure that every predictive model entering production is robust, correct, and maintainable, regardless of how it was originated.
• The team combines quantitative development engineers aligned with research scopes and a platform team providing shared infrastructure (model registry, CI/CD, AI tooling, monitoring).
• Our models power real trading decisions. What we validate directly impacts performance.
The Role
• You will be an engineering partner to quantitative researchers. Your job is to ensure that predictive models are fit for production, testing them, challenging them, deploying them, and monitoring them at scale.
• The most important part of the job is judgment, understanding what a model does, identifying what could go wrong, and having the confidence to say "not yet" when something isn't right.
What you will do
• Validate models independently, run quality tests, identify implementation issues, provide written quality assessments before deployment
• Challenge implementations, engage with researchers on design choices, architecture, and robustness. Ask "why?" and "what could break?"
• Deploy and monitor at scale, manage production predictors, detect drift and divergence, recommend retirement when models no longer meet standards
• Build quality infrastructure, contribute to automated testing frameworks, monitoring, and the tools that make quality systematic
• Understand model logic, develop functional expertise in model families so you can validate from a domain perspective as well as a technical one
What you will NOT do
• Wait for researchers to hand you finished code and just deploy it
• Work in isolation from the people who design the models
• Treat every model the same regardless of complexity or novelty
• Accept "it runs" as a sufficient standard for production
Who We're Looking For
• We're open to diverse backgrounds. The best person for this role might come from quantitative development, model risk management, Data scientist, scientific research, reliability engineering, or quantitative research itself. What matters is the mindset.
Core traits (non-negotiable)
• Intellectual assertiveness, you're comfortable raising concerns about implementations, including with senior colleagues. You see this as part of delivering quality.
• Quality instinct, you get genuine satisfaction from finding problems others missed. Your best day at work is the day you prevented a flawed model from reaching production.
• Curiosity about why, you want to understand what a model does, how it behaves, and what assumptions it relies on.
• Production discipline, you care about code that's maintainable, testable, and robust under edge cases.
Technical skills
• Python (advanced), you write production-quality code and can read and assess code written by others
• Statistical reasoning, hypothesis testing, out-of-sample validation, understanding of overfitting and multiple comparisons
• Production systems, Linux, CI/CD, containerisation, cloud. Comfort with systems that run continuously.
• Data at scale, experience with large datasets, data pipelines, data quality issues
• Bonus, experience with model validation frameworks, time series analysis, ML model evaluation, or quantitative finance
Experience
• 4–8 years in a demanding quantitative or engineering environment. Relevant backgrounds include,
• Quantitative developer or production quant at a systematic fund, bank, or fintech
• Model validation / model risk management at a financial institution
• Reliability engineer, quality engineer, or ML engineer at a tech company
• Quantitative researcher who prefers validating and improving models over building new ones
• PhD in experimental science with post-academic quantitative experience
• Excellent command of English. French is a strong plus.
What Makes This Role Different
• You're a partner. Researchers value your engineering perspective and actively welcome challenge on their implementations.
• Your judgment matters more than your code. As AI generates more code, the durable value is engineering judgment and domain expertise. We're building toward that future now.
• You see the full picture. Your team monitors thousands of models across multiple strategies. You see patterns individual researchers can't, and that cross-model visibility is part of your value.
• Direct impact. When you catch a flawed model, the impact is real and measurable. Quality is what protects the firm's performance.
• You help define the standards. The quality framework is being built now. You won't just follow a process, you'll shape it.
Our Interview Process
• Our interview process tests judgment and technical depth. Expect to discuss how you think about model quality, how you approach code you didn't write, and how you navigate situations where your assessment differs from others'. We're interested in how you think.
• CFM is an equal opportunity employer committed to non-discrimination. We welcome applications from all backgrounds.
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.
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