Full time

Responsibilities

• Designing and operating data pipelines and data models for structured and unstructured financial data, including ingestion, transformation, storage, and data quality controls.
• Developing and maintaining robust, production-grade software systems, libraries, services, and web-based applications that support quantitative research, portfolio analytics, and AI-driven applications.
• Designing, integrating, and deploying end-to-end AI and machine-learning solutions, with a focus on serving GenAI and machine-learning models within data and software platforms.
• Collaborating closely with quantitative researchers and portfolio managers to implement, extend, and operationalize quantitative models, portfolio construction methods, and optimization techniques.
• Translating research concepts into scalable, maintainable, and testable implementations suitable for enterprise use.
• Building end-to-end solutions, from data ingestion through modeling, backend services, APIs, and user-facing web interfaces.
• Operating solutions in a cloud-native environment, including containerization, CI/CD workflows, monitoring, and lifecycle management.
• Supporting internal and client-facing presentations by clearly explaining data flows, model logic, and technical architectures.

Experience

• MSc (or equivalent practical experience) in computer science, software engineering, data engineering, applied mathematics, or a closely related technical field.
• Strong software engineering skills, with experience designing modular, maintainable, and production-ready systems, primarily in Python.
• Full-stack development experience, including:
• Hands-on experience with at least one modern frontend framework (e.g. React, Vue, or Angular),
• Experience building backend services, ideally in Python, including APIs that serve data, analytics, and AI/ML models.
• Strong hands-on experience in data engineering, including database design, schema modeling, and building reliable data pipelines.
• Practical experience serving AI and machine-learning models (e.g. inference pipelines, GenAI/LLM-based systems).
• Solid quantitative literacy, with the ability to understand, implement, and extend quantitative models used in portfolio analytics, optimization, or risk management.
• Experience working across the full data–model–application lifecycle, rather than in a narrowly specialized role.
• Familiarity with cloud infrastructure, containerization, and CI/CD workflows.
• Strong analytical and problem-solving skills; results-oriented, self-motivated, eager to learn, and a collaborative team player.
• Ability to communicate and present complex topics clearly and concisely, adapting to different audiences.
• Fluency in English is required; good knowledge of German and/or French is a plus.

• Experience designing and operating data platforms for quantitative research, analytics, or AI use cases.
• Prior experience in a quantitative, financial, or asset management environment.
• Strong theoretical background in statistics, stochastic processes, numerical mathematics, or optimization techniques.
• PhD in a quantitative field (e.g. applied mathematics, statistics, physics, computer science, or quantitative finance).
• Hands-on experience in AI development, including LLMs, prompt/context engineering, and agentic workflows.
• Exposure to portfolio construction, optimization, risk modeling, or asset-liability management concepts.
• Practical knowledge of Azure cloud services and infrastructure-as-code tools (e.g. Terraform).
• Experience acting as a technical bridge between quantitative researchers, portfolio managers, and engineering teams.
• Experience in leveraging AI for coding and software development.