Quant Developer - AHL Technology

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About Man Group Man Group is a global alternative investment management firm focused on pursuing out performance 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.

As a Quant Developer at Man Group you will be part of one of our flagship teams in Man’s systematic trading arm – Man AHL. The team works hand‑in‑hand with Quant Researchers focusing on equity market neutral models. Your challenges will be varied, and will involve building new alphas and new trading strategies.

Quant Developers at Man Group are all part of our broader technology team, members of a group of over sixty individuals representing eighteen nationalities. We have varied backgrounds including Computer Science, Mathematics, Physics, Engineering – even Classics – but what unifies us is a passion for technology and writing high‑quality code. Our developers are organised into small cross‑functional teams, and our engineering roles broadly fall into two kinds: “Quant Platform Developers” or “Core Techs”, and “Quant Developers”, also referred to as “Sector Techs”.

We use the term “Sector Tech” because some of our teams are aligned with a particular asset class or market sector. People often rotate teams to learn more and find the position that best matches their interests.

Our systems are almost all running on Linux and most of our code is in Python, with the full scientific stack:
Num Py, Sci Py, Pandas, scikit‑learn to name a few of the open‑source libraries we use extensively. We implement the systems that require the highest data throughput in Java and C++. We use 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 and Kubernetes for containerisation, Open Stack for our private cloud, Ansible and Terraform for architecture automation, and Slack for internal communication.

We heavily utilise Arctic

DB ((Use the "Apply for this Job" box below).) – our in‑house developed Data Frame Database. Our technology list is never static; we constantly evaluate new tools and libraries.

At AHL we strive to hire only the brightest and best and most highly skilled and passionate technologists.

• Exceptional technology skills; recognised by your peers as an expert in your domain
• A keen interest and understanding of financial markets and instruments
• A proponent of strong collaborative software engineering techniques and methods: agile development, continuous integration, code review, unit testing, refactoring and related approaches
• Strong knowledge of Python
• Proficient on Linux platforms with knowledge of various scripting languages
• Experience of data analysis techniques along with relevant libraries e.g. Num Py, Sci Py, Pandas
• Relevant mathematical knowledge e.g. statistics, asset pricing theory, optimisation algorithms

• Experience of front‑office quantitative software development e.g. in a hedge fund or investment bank
• Experience of web‑based development and visualisation technology for portraying large and complex data sets and relationships
• Experience of machine learning techniques, natural language processing and related libraries and frameworks e.g. scikit‑learn, Tensor Flow

• Strong academic record and a degree with high mathematical and computing content e.g. Computer Science, Mathematics, Engineering or Physics from a leading university
• Craftsman‑like approach to building software; takes pride in engineering excellence and instils these values in others
• Demonstr…