Software Engineer

The role

As a software engineer in the Front-Office Engineering organization at Man Systematic, you will work closely with quantitative researchers and portfolio managers. Your responsibilities will be diverse and may include onboarding new datasets, developing new computer-based frameworks, improving our research platform, and optimizing existing code using efficient numerical algorithms and cluster computing solutions.

Our technology

Almost all of our systems run on Linux, and most of our code is written in Python, using the full scientific stack. Nearly all of Man’s trading systems run on-premises. We implement the systems that require the highest data throughput in Java. For storage, we rely heavily on MongoDB and H2O.ai. We use Confluent & Apache Kafka for data pipelines, Bitbucket for source code management, Jenkins for continuous integration, Grafana & Prometheus for metrics collection, ELK for log shipping and monitoring, Docker for containerization, OpenShift for our private cloud, Ansible for automation, and APIs for seamless communication. Our technology stack is not static; we continuously evaluate new tools and libraries.

Essential requirements

• 2–3 years of professional experience in software development, preferably with a focus on quantitative aspects
• Proficiency in Python and experience with scientific libraries, including Pandas, NumPy, SciPy, statmodels, and Sub-Teams
• Experience working on production systems, with an understanding of best practices in testing, monitoring, and deployment
• Proficient in working on Linux platforms and using DevOps technologies such as MS SQL, Postgres, or MongoDB
• Working data in one or more relational databases

Advantages

• Experience in quantitative software development in a front-office environment, such as at a hedge fund, a proprietary trading firm, or an investment bank
• Experience working with large datasets, both structured and unstructured
• Experience with Java or similar languages
• Experience building web applications using modern frameworks such as React
• A degree in computer science, electrical engineering, mathematics, statistics, econometrics, or an equivalent field
• Knowledge of modern data engineering practices, including data pipeline and ETL tools, distributed storage and processing, and data warehousing
• Strong understanding of financial markets and instruments
• Experience working with financial market data
• Relevant mathematical knowledge, e.g., statistics, linear algebra, analysis

Personal characteristics

• A strong academic background and a degree with a significant focus on mathematics and computer science, such as computer science, software development, or physics
• Individually resilient with a highly analytical approach to problem-solving
• Self-organizing and flexible, capable of handling multiple projects and managing competing business requirements and priorities
• A desire to collaborate with multiple teams to improve processes
• Strong interpersonal skills to build close working relationships with quantitative researchers, portfolio