Job Opportunities API

About Man Group Man Group is a global alternative investment management firm focused on pursuing outperformance for sophisticated clients via our Systematic, Discretionary and Solutions offerings. Powered by talent and advanced technology, our single and multi-manager investment strategies are underpinned by deep research and span public and private markets, across all major asset classes, with a significant focus on alternatives. Man Group takes a partnership approach to working with clients, establishing deep connections and creating tailored solutions to meet their investment goals and those of the millions of retirees and savers they represent. Headquartered in London, we manage $228.7 billion* and operate across multiple offices globally. Man Group plc is listed on the London Stock Exchange under the ticker EMG.LN and is a constituent of the FTSE 250 Index. Further information can be found at www.man.com At Man Group, we respect your privacy and we are committed to protecting and safeguarding your Personal Data. We have developed policies and processes which are designed to provide for the security and integrity of your Personal Data. We are committed to Processing your Personal Data fairly and lawfully, and being open and transparent about such Processing. For further information on how we process your data, please see the privacy notice for applicants  here * As at 31 March 2026   The Role As a 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 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. Key Competencies Essential 3+ years of professional experience in software engineering, preferably with a focus on quantitative applications Proficiency in Python and experience with scientific libraries including Pandas, NumPy, SciPy, statsmodels and scikit-learn Experience working on production systems, with an understanding of best practices for testing, monitoring, and deployment Comfortable working on Linux platforms and using Git Working knowledge of one or more relevant database technologies, such as MS SQL, Postgres, or MongoDB Advantageous Experience in quantitative software development within a front-office setting, such as at a hedge fund, proprietary trading firm, or investment bank Experience working with large data sets, both structured and unstructured 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 Rele