Senior Quant Developer

Man Group•New York, NY

5h•Onsite

About The Position

Man Group is a global alternative investment management firm focused on pursuing outperformance for sophisticated clients via its Systematic, Discretionary and Solutions offerings. Powered by talent and advanced technology, its 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, it manages $227.6 billion and operates 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. The Discretionary & Client Solutions Technology team consists of approximately 35 engineers across London, New York, and Sofia, building the platforms that power Man Group's discretionary investment and client-facing operations. They operate at a high tempo, shipping multiple production releases weekly across portfolio analytics, fund data platforms, client reporting, data pipelines, dashboards, and AI tools. This team is one of the most active AI-adopting engineering teams in Man Group, building LLM-powered agents, research tools with vector search, and AI integrations that portfolio managers and analysts use daily. The role offers the opportunity to shape how AI is integrated into investment workflows. This is a senior engineering role sitting at the intersection of Man Group's Discretionary (public markets) and Solutions technology teams in New York. The individual will be the primary NY-based engineer supporting portfolio managers, analysts, and quant functions across both business lines. The role spans Python data pipelines and analytics platforms serving Discretionary investment teams (equities, credit, and alternatives) alongside Python systems powering Solutions' fund analytics, portfolio management, and reporting infrastructure. The individual will work hands-on building and operating production systems while acting as the key technical liaison for NY-based stakeholders.

Requirements

5+ years of professional software engineering experience with a strong delivery track record
Strong Python: Well-tested, modular production code. Comfortable with dataclasses, type hints, OOP, design patterns
Financial services experience — particularly portfolio management, risk, or investment operations
Pandas/NumPy proficiency: Building efficient data pipelines and transformations at scale
AI/LLM tooling — experience building with or integrating LLM-based tools. We're active early adopters of AI-assisted development and are building AI agents for our investment teams
Solid testing discipline: pytest, unit tests, integration tests — you write tests as a matter of course
Comfortable in a Linux environment with Git, virtual environments, and CLI tooling
Strong communicator: You can explain technical decisions to non-technical stakeholders concisely, and you view stakeholder interaction as integral to good engineering — not overhead
Self-directed: You manage your own priorities, flag blockers early, and drive work to completion with appropriate autonomy
Degree in a STEM subject or equivalent practical experience

Nice To Haves

React/TypeScript for frontend development
Experience with web APIs: Building and consuming RESTful services (FastAPI, Flask, or equivalent)
C#/.NET familiarity — some Solutions systems are built in .NET. You won't be the primary .NET engineer, but being able to read and navigate the codebase is valuable
Kubernetes & containerisation — deploying and troubleshooting services in k8s
Airflow — building and maintaining scheduled data pipelines
Distributed systems concepts — messaging architectures, event-driven design, caching strategies
Experience working with geographically distributed teams — London will be your primary collaboration hub

Responsibilities

Build, extend, and maintain Python data pipelines using Pandas, NumPy, and internal libraries
Develop and enhance portfolio analytics, risk reporting, and fund data platforms
Contribute to AI-powered tools for investment teams — research databases with vector search, AI integrations for portfolio managers and analysts
Build and maintain FastAPI and Flask backends and React/TypeScript frontends using our shared component libraries
Deploy services to Kubernetes; manage Airflow DAGs for scheduled data workloads
Collaborate with London and Sofia-based engineers on cross-team initiatives
Serve as the primary technical contact for NY-based Discretionary and Solutions stakeholders
Gather requirements, translate business needs into technical solutions, and manage expectations
Work directly with portfolio managers, analysts, and quants — understanding their workflows is as important as writing the code
Own production stability for systems in your portfolio — investigate data quality issues, triage support requests, manage incident response
Maintain documentation and operational processes