Python Engineer - Numeric

About Man Numeric

Man Numeric is a fundamentally-driven systematic investment manager. It takes a bottom-up approach to research and security selection and distils its findings into systematic processes that are applied across regions, styles and capitalisations. It offers long-only and alternative investment strategies that invest in both equity and credit markets.

The firm’s research-driven culture, which is underpinned by advanced technology and data science techniques, focuses on innovation and enhancement across alpha generation, risk management, portfolio construction and implementation.

Man Numeric has capabilities in systematic responsible investing, offering dedicated quantitative strategies and incorporating proprietary ESG and Climate alpha signals across a diverse range of strategies.

Founded in 1989 and becoming part of Man Group in 2014, Man Numeric’s assets under management were $39.4 billion at 30 September 2023. Further information can be found at www.man.com/numeric.

The Role

As a Python Engineer in the Front-office Engineering organization at Man Numeric, 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 Technology

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.

Essential

• 3-5 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

Advantageous

• 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

Personal Attributes

• 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