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Head of Prediction Platform
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Paris, 75, FR
**ABOUT CFM**:
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.
**Job Summary / Mission**
The Prediction team collaborates with the Research team to develop and maintain the Prediction models (“alphas”) used to make decisions for CFM automated trading systems.
CFM is seeking a dynamic and experienced **Head of Prediction Platform** to lead the build-out, management, and optimization of its Prediction platform, encompassing shared services and components (e.g. alpha models performance monitoring, common analytics libraries, software factory), Prediction’s cloud-based HPC engines and on-premises infrastructure.
**Key Responsibilities**
- Oversee the development and maintenance of a secure, robust, and high-performance Prediction platform and infrastructure that supports operating Prediction models at scale.
- Work closely with Quant Developers to understand their needs and ensure seamless integration of Prediction model pipelines and processes into the platform, from development to production.
- Supervise the evolutions of shared components and services used by Quant Developers and Researchers, in particular: common analytics libraries, model testing and monitoring frameworks.
- Foster a product-centric approach, using feedback loops to continuously improve the expected features, usability, and performance, ensuring the platform is a value-add to internal users.
- Optimize developer workflows and processes through automation and reusable templates, reducing the time to production.
- Manage a team of Software engineers working on the Prediction platform.
- Propose, implement and monitor Key Performance Indicators, in relation to Business added value.
- Collaborate efficiently with Core Technology and Security teams to smoothly integrate into CFM technological ecosystem, stack and standards.
- Ensure globally the efficiency and relevance of the Prediction platform to CFM business needs, define and pilot the related roadmap.
**Qualifications / Required skills**
- Bachelor's or Master's degree in computer science, software engineering, or related field.
- Minimum of 10 years of experience in HPC engineering, with at least 5 years in a hands-on leadership role.
- Proven track record of managing hybrid compute environments in challenging contexts, driving the adoption of cloud technologies and designing platforms with a developer-first, self-service mindset.
- Strong knowledge of cloud technologies (AWS S3, Batch, Sagemaker, CloudWatch...) and containerization (Docker).
- Proficient in Python including numerical libraries (numpy, pandas, scikit-learn ), and software craftmanship (code quality, test-driven development, continuous integration, change and release management,...).
- Proven achievements with Platform Engineering tools and methodologies, including CI/CD pipelines, Infrastructure-as-Code (CloudFormation), and self-service capabilities.
- Excellent leadership, team management and project management skills, applicable to cross-functional contexts.
- Ability to solve complex problems and make strategic decisions.
- Excellent communication skills, with the faculty to translate technical concepts into business outcomes for non-technical stakeholders.
- Capacity to work in a fast-paced, high-stakes environment, with a commitment to professional development and continuous learning.
- Knowledge of financial markets, governance and regulatory requirements and/or quantitative research is a plus.
**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.
