Senior Quantitative Developer

As a Senior Quantitative Developer in the Front-office Engineering organization at Man Systematic, you will work closely with Quantitative Researchers and Portfolio Managers. Your challenges will be varied and may include onboarding new datasets, implementing new trading signals, developing portfolio optimization tools, building data visualization frameworks, enhancing our research platform, and performance tuning existing code using efficient numerical algorithms and cluster-computing solutions.

Our systems are almost all running on Linux and most of our code is in Python, with the full scientific stack: NumPy, SciPy, Pandas, statsmodels, and scikit-learn to name a few of the libraries we use extensively. We implement the systems that require the highest data throughput in Java. For storage, we rely heavily on MongoDB and MS SQL.

We use Control-M and Airflow for workflow management, Kafka for data pipelines, Bitbucket for source control, Jenkins for continuous integration, Grafana + Prometheus for metrics collection, ELK for log shipping and monitoring, Docker for containerisation, OpenStack for our private cloud, Ansible for architecture automation, and Slack for internal communication. Our technology list is never static: we constantly evaluate new tools and libraries.

Man Systematic has a small company, no-attitude feel. It is flat structured, open, transparent and collaborative, and you will have plenty of opportunity to grow and have enormous impact on what we do.  We are actively engaged with the broader technology community.

• We regularly talk at leading industry conferences across the globe
• We host and sponsor Boston meetups and London’s PyData and Machine Learning Meetups
• We open-source some of our technology, including our ultra-high-performance DataFrame database, ArcticDB. See https://github.com/man-group for a full list and more information.

We’re fortunate enough to have a fantastic open-plan office overlooking the Boston harbour, and continually strive to make our environment a great place in which to work. We believe that agile working allows us to deliver the best business outcomes for our clients and investors as well as having a positive impact on work-life balance and the wellbeing of our staff. We are simultaneously committed to “levelling the playing field” and believe that agile working promotes inclusivity across teams, regions and business units. In general, Technology roles are expected to be in the office for 3 days a week. However, the specifics can vary based on the role, team, and individual circumstances, and are ultimately subject to the manager’s discretion.

We offer competitive compensation, a generous holiday allowance, various health and other flexible benefits. We are also committed to continuous learning and development via coaching, mentoring, regular conference attendance and sponsoring academic and professional qualifications.

We strive to hire only the brightest, best and most highly skilled, passionate technologists.

• 5-7 years of professional experience in software engineering, preferably with a focus on quantitative applications
• Expert knowledge of Python and Pandas and proficiency with related scientific libraries including NumPy, SciPy, statsmodels, and scikit-learn
• Experience developing mission-critical production systems, with knowledge of best practices for testing, monitoring, and deployment
• Proficient on Linux platforms and strong understanding of Git
• Working knowledge of one or more relevant database technologies, such as MS SQL, Postgres, or MongoDB
• Demonstrated experience working with large data sets, both structured and unstructured

• Experience in quantitative software development within a front-office setting, such as at a hedge fund, proprietary trading firm, or investment bank
• Experience mentoring junior team members and managing projects
• Experience building web applications using modern frameworks like React
• Proficient with distributed computing technologies such as Spark, Dask, Kubernetes, Redis Knowledge of modern data engineering practices including data pipeline & ETL tools, distributed storage & processing and data warehousing
• Strong understanding of financial markets and instruments
• Experience working with financial market data
• Relevant mathematical knowledge e.g., statistics, time-series analysis

• Strong academic record and a degree with high mathematical and computing content e.g., Computer Science, Mathematics, Engineering or Physics
• Intellectually robust with a keenly analytic approach to problem solving
• Self-organised with the ability to effectively manage time across multiple projects and with competing business demands and priorities
• Focused on delivering value to the business with relentless efforts to improve process
• Strong interpersonal skills: able to establish and maintain a close working relationship with quantitative researchers, portfolio managers, traders and senior business people alike
• Confident communicator: able to argue a point concisely and deal positively with conflicting views