Software Engineer

The role

As a software engineer within the Front Office Engineering organization at Man Systematic, you will work closely with quantitative researchers and portfolio managers. Your key responsibilities include:

• Implementation of New Trading Signals
• Development of Portfolio Optimization Tools
• Creating Data Visualization Dashboards
• Implementation of New Analytics Platforms
• Optimizing existing code using efficient numerical algorithms and traditional computing solutions

Our technology

Our systems are written almost entirely in Python, using the full scientific stack. Most of our code is written in Python, using the full scientific stack: NumPy, SciPy, Pandas, and scikit-learn. We also use technologies that require the highest level of expertise in Java. For storage, we rely heavily on MongoDB and H2G. We use Control-M and Airflow for workflow management, GitLab for data pipelines, Bitbucket for source code management, Docker for internal systems, Prometheus & Grafana for metrics, the ELK Stack for log management, Docker for automation, and Ansible for infrastructure automation. Slack is used for internal communication. Our technology stack is not static; we are constantly evaluating new tools and technologies.

Essential requirements

• 2+ years of professional experience in software development, preferably with a focus on quantitative applications
• Python and experience with scientific libraries, including Pandas, NumPy, SciPy, statemachines, and scikit-learn
• Experience working with production systems, with an understanding of best practices in testing, monitoring, and deployment
• Familiarity with Linux and the use of tools such as Git, Hadoop, Kafka, Apache Spark, and NoSQL
• Practical knowledge of one or more relevant data science technologies, such as R, Julia, Python, or MongoDB

Advantages

• Experience in quantitative software development in a front-office environment, such as a hedge fund, a proprietary trading firm, or similar
• Experience working with large volumes of data, both structured and unstructured
• Experience working with applications that use modern tools and frameworks such as Bread
• Knowledge of distributed computing technologies such as Spark, Disk, Kubermesh, and Redis
• Knowledge of modern data engineering practices, including data pipeline and ETL tools, distributed storage and messaging systems, and data warehousing
• Strong understanding of financial markets and instruments
• Experience working with real-time market data
• Relevant mathematical knowledge, e.g., statistics, time series analysis

Personal characteristics

• Strong academic record and a degree with a strong focus on mathematics and computer science, such as computer science, mathematics, engineering, or physics
• Technically curious with a strong desire to solve problems
• Self-organized, with the ability to effectively manage time across multiple projects and competing demands
• Focused on delivering value to the company through tireless efforts to improve processes