Quantitative Developer

Quantitative Developer – Tier 1 Front Office Team

Location: New York / London

Experience: 1 - 10 YOE

We are working with a top-tier global investment firm who is looking to add to their engineering team.

You’re probably doing well. But are you still accelerating?

Most strong engineers don’t start looking because their job is bad. They start looking because the learning curve has flattened. The feedback loop has slowed down. The work is still respectable… but it’s no longer compounding.

This opportunity sits within an organization that has made quantitative engineering central to how research is developed, portfolios are constructed and investment decisions are implemented. The team is growing because small improvements are no longer enough.

The Work

You’ll sit directly inside the investment process.

Not supporting it from a distance. Not waiting for requirements to arrive through several layers of stakeholders.

You’ll work alongside quantitative researchers, portfolio managers and other engineers on systems used to research, evaluate and implement live investment strategies.

Your work may include:

• Building research and analytical tools used across fundamental and systematic equities teams
• Developing systems supporting signal research, portfolio construction, optimisation, risk and execution
• Turning research ideas into production-grade tools that are scalable, measurable and reliable
• Building backtesting, simulation and portfolio analytics frameworks
• Working with market, fundamental, alternative and portfolio datasets
• Improving how investment ideas move from research into live portfolios
• Developing tools for risk analysis, hedging, performance attribution and post-trade evaluation
• Owning systems through design, implementation, deployment and continued improvement

You’ll see your work used by the people making investment decisions. The feedback loop is real. So is the accountability.

This isn’t an enablement role

You won’t be measured by how many tickets you close or how many internal tools you maintain.

You’ll be measured by whether the systems you build improve the research process, make investment decisions better or allow teams to operate faster and more effectively.

When a problem matters, you’ll be expected to understand it properly and take ownership of the outcome.

Let’s be honest about the trade-offs

This environment won’t suit everyone.

The expectations are high.

Your work is visible.

You’ll receive direct feedback, and the standard will continue moving as you improve.

There’s limited hand-holding, and you won’t always be given a perfectly written specification.

In return:

• Decisions are made quickly
• Strong ideas are taken seriously
• Engineers have genuine ownership
• You’ll work closely with experienced investors and researchers
• Progression is driven by contribution rather than tenure

People who perform well here don’t wait for someone to decide they’re ready. Their scope grows because they repeatedly prove they can handle more.

You’re likely to be a fit if

• You’re strong in Python and care about writing correct, durable software
• You enjoy problems that sit between engineering, quantitative research and decision-making
• You want to understand why a system is being built, not simply what the requirements say
• You’re interested in portfolio construction, optimisation, risk, execution or systematic research
• You’re comfortable working with complex datasets and ambiguous problems
• You value short feedback loops and proximity to the end user
• You’re early in your career but already thoughtful about where you invest your time

Experience in finance is helpful, but it isn’t the only filter. Strong candidates may come from technology companies, research organisations, quantitative firms or other environments where engineering quality and analytical thinking genuinely matter. Trajectory, judgement and ownership carry more weight than having the perfect background.

A useful question to ask yourself

If your responsibilities, learning curve and level of impact stayed exactly the same for the next 12 months… would you be satisfied?

If the answer isn’t an immediate yes, that hesitation is probably worth exploring.