Senior Quantitative Data Scientist/Engineer

Opportunity Overview

We are seeking an experienced Quantitative Data Scientist/Engineer to drive the construction and
maintenance of our automated asset-management platform. This includes everything from predictive signals
and risk models to production dashboards and client-facing analytics. You will collaborate closely with
quantitative researchers and data scientists/engineers, transforming high-impact ideas into robust, revenuegenerating systems. This role merges deep quantitative research with hands-on software engineering to
advance our portfolio construction strategies.


Key Responsibilities

•  Research & Development: Utilize advanced statistics, machine learning, and information-theoretic methods for analyzing financial time-series data to develop investment strategies.
•  Model Evaluation & Testing: Design experiments, perform back-tests, and create diagnostics to assess the performance and stability of predictive models.
•  Data Engineering: Enhance and sustain Python-based data pipelines and research infrastructure, ensuring data flows are clean, reliable, and thoroughly documented.
•  Code Quality: Develop and review high-standard production Python code, optimizing existing modulesfor improvement, clarity, and maintainability.
•  Collaboration: Collaborate with quantitative researchers to convert concepts into operational prototypes and production-ready solutions.

Essential Qualifications

•  3+ years in a data science or software engineering position.
• MSc or PhD in a relevant quantitative field, such as Data Science, Computer Science, AI, Statistics, Mathematics, Physics, Engineering, or Finance.
•  Experience with time-series analysis including feature engineering, evaluation, and back-testing.
•  Proficient in Python (including pandas, NumPy, scikit-learn), version control via Git, and SQL skills.

Preferred Qualifications (at least 50% required)

•  Strong software engineering fundamentals, including testing protocols, design patterns, and system architecture.
•  Experience with SQL databases (such as PostgreSQL, SQL Server).
•  Experience in designing and deploying ML models for signal generation, risk modeling, and portfolio recommendations; manage the full lifecycle from experimentation to production deployment.
•  Implement MLOps workflows involving packaging, validation, CI/CD processes, containerization, orchestration, and monitoring to ensure reliable production models.
•  Contribute to the development of the platform stack, including Django-based services, PostgreSQL/SQL Server layers, and comprehensive end-to-end pipelines, focusing on writing, reviewing, and enhancing production code, tests, and monitoring systems.