July 2026Hilal Kilinc4 min read
How AI Is Reshaping Quantitative Research and Hiring
Machine learning has been embedded across hedge funds, proprietary trading firms and systematic investment managers for many years. Techniques such as regression models, decision trees and neural networks are already established within quantitative investing, alongside statistical learning approaches that have been used for decades.
The more recent development is the rise of generative AI and large language models, and the ways firms are using these technologies to support research automation, process growing volumes of information and increase research efficiency.
Hilal, a Consultant at Selby Jennings specialising in quantitative research, analytics, and trading across Europe, sees this trend reflected in conversations across the market.
"Machine learning is now part of the discussion at almost every quantitative firm I speak with. The variation tends to be in where it is being applied, the maturity of the underlying infrastructure, and the scale of investment firms are making in the area," she says.
AI is no longer viewed solely as a future opportunity. It is increasingly being used to improve research workflows, support alpha discovery, analyse expanding datasets and increase efficiency across the research lifecycle. As a result, demand continues to grow for quantitative professionals who can combine strong mathematical and statistical foundations with modern machine learning expertise.
The evolution of Quantitative Research
Quantitative investing has traditionally relied on statistical models, factor research, market microstructure analysis and mathematical frameworks. Researchers often enter the industry from mathematics, physics, statistics and computer science backgrounds, and these core skill sets remain highly relevant today.
Machine learning is not a new discipline within quantitative finance. Many firms have used machine learning techniques as part of their investment and research processes for more than a decade.
What has changed is the range of tools available, the scale of the datasets firms can analyse and the growing role of generative AI within the research process. Rather than existing as a separate discipline, these technologies are being used alongside established quantitative techniques to analyse larger and more complex datasets, identify patterns that may be difficult to capture using conventional methods alone and accelerate parts of the research process.
Across the industry, firms are applying machine learning in a variety of ways, including:
- Signal discovery and alpha research
- Alternative data analysis
- Portfolio construction and optimisation
- Research automation and workflow efficiency
"Most firms are looking at how machine learning can complement existing quantitative techniques, improve research efficiency, and help researchers identify opportunities within increasingly large and complex datasets," says Hilal.
The rise of generative AI and large language models
Generative AI and large language models are becoming an increasingly common topic within quantitative research teams. Unlike many traditional machine learning techniques, which are primarily designed to identify patterns within structured datasets, LLMs offer new ways to process, organise and interact with unstructured information.
This is particularly relevant as firms continue to explore growing volumes of data and look for ways to automate parts of the research process.
Researchers are exploring how generative AI can:
- Review and summarise academic research
- Support idea and hypothesis generation
- Extract information from unstructured data
- Translate research methodologies into initial code
- Build and test early-stage backtests
- Run validation and robustness checks
- Summarise findings for researcher review
- Improve research workflows
The current frontier is increasingly focused on research workflow automation. A future research process could involve AI reviewing a newly published academic paper, replicating its methodology, generating initial code, testing the findings on out-of-sample data and assessing variations before returning the results to a researcher.
The researcher would still be responsible for evaluating whether the results make sense, identifying weaknesses, deciding which tests to run next and determining if the idea is worth
While interest in the technology continues to grow, most firms are still assessing where LLMs can deliver additional value. Rather than replacing established research methodologies, they are more commonly being used to automate processes and complement them.
"What I am seeing is a clear focus on efficiency," says Hilal. "Firms are looking at how generative AI can help researchers work more efficiently, manage large volumes of data, and streamline parts of the research process, while continuing to rely on established quantitative methods for research, modelling, and strategy development."
The changing role of the quantitative researcher
As firms adopt generative AI tools, the role of the quantitative researcher may begin to change.
Researchers have traditionally spent a significant amount of time implementing methodologies, writing code, building backtests and running strategies. AI is increasingly capable of supporting parts of this implementation work.
This does not remove the need for quantitative researchers. Instead, it may allow them to spend more time on:
- Hypothesis generation
- Research direction
- Identifying weaknesses
- Applying judgement
- Deciding which ideas are worth pursuing
The shift is less about replacing researchers and more about increasing research throughput. A researcher who previously had time to investigate one paper or idea in depth may be able to assess several with AI assistance, while retaining responsibility for the quality and direction of the research.
The talent shift
As technology develops, hiring strategies are changing. The traditional quant researcher profile remains highly valued, and firms still want strong mathematical ability, statistical judgement and an understanding of financial markets.
However, many hiring managers are also looking for candidates with experience in:
- Machine learning
- Deep learning
- Reinforcement learning
- Natural language processing
- Large language models
- TensorFlow and PyTorch
- AI-focused research infrastructure
- Research automation tools
This is creating stronger demand for researchers who can work across quantitative finance. Current quantitative research jobs show how roles are increasingly combining established statistical modelling techniques with machine learning and AI-supported research processes.
"Technical depth remains essential, but hiring managers are increasingly looking for researchers who can combine strong quantitative foundations with experience working with modern machine learning techniques," says Hilal. "Many firms are exploring where these tools can improve parts of the research process, whether that's identifying patterns in datasets, improving approaches in strategies or increasing research efficiency. As a result, candidates who can bridge traditional quantitative research with newer technologies are attracting significant attention."
A broader source of talent
As machine learning and generative AI become more embedded within quantitative research, firms are increasingly looking beyond traditional quantitative finance backgrounds. While experience in quantitative research or trading remains highly valued, employers are also assessing candidates whose expertise has been developed in adjacent fields.
Relevant talent can come from a range of environments, including:
- Hedge funds and proprietary trading firms
- Investment banks
- Technology companies
- Research institutes and laboratories
- Academic institutions
This has created opportunities for researchers who may not have followed a conventional route into financial markets. Experience in machine learning often develops transferable skills, including statistical modelling, predictive analytics, optimisation and the ability to work with large and complex datasets more efficiently.
However, success in financial markets requires more than technical expertise alone. Market data is often noisy, factors contributing to strategy performance can evolve over time and even sophisticated models must be robust enough to perform in dynamic and competitive environments.
As a result, firms are evaluating a candidate's ability to apply quantitative thinking to real research problems, challenge results and make sound decisions about which ideas should progress.
"The strongest candidates are often those who can combine deep technical expertise with strong quantitative research skills," Hilal notes. "Machine learning experience can be highly valuable because it develops capabilities in areas such as statistical modelling, handling large and complex datasets, experimentation and optimisation. Beyond the direct techniques themselves, this experience often equips researchers with a broader toolkit for tackling challenging research problems and identifying solutions to issues firms may be facing. Ultimately, firms are less interested in the specific technologies a candidate has used and more interested in how they approach research, test hypotheses, generate ideas and apply those skills to real-world investment challenges."
The challenge
The use of machine learning and generative AI presents several challenges. While advances in computing power and AI tooling have expanded the range of techniques available to researchers, successfully applying these methods in financial markets remains far from straightforward.
Common challenges include:
- High infrastructure and computational costs
- Model interpretability and explainability
- Adapting models to changing market regimes
- Balancing model complexity with robustness
- Integrating new tools into existing research frameworks
In financial markets, even well-designed models can struggle when market conditions change or historical relationships break down. AI-generated research must also be reviewed carefully, particularly when tools are producing code, replicating methodologies or summarising findings.
Human judgement remains central to deciding whether results are credible, identifying methodological weaknesses and assessing how findings may perform under different market conditions.
"Machine learning is becoming an increasingly demanded part of the quantitative research landscape," says Hilal. "Firms are looking beyond familiarity with specific techniques and focusing on how candidates have applied them in practice. The key question is not simply whether someone can build a model, but whether they can use quantitative and machine learning methods to investigate research questions, generate robust insights and contribute to the investment process."
As these technologies continue to develop, the challenge for firms is identifying where they can produce a measurable improvement to research quality, speed and productivity.
What comes next
Demand for machine learning expertise is likely to remain an important part of quantitative hiring. Selby Jennings has identified AI, quant engineering and broader business influence as key areas shaping quant careers beyond 2026.
Firms are increasingly open to candidates from different backgrounds, including:
- Machine learning PhD graduates exploring quantitative finance
- Quant researchers looking to apply deeper machine learning expertise
- AI and machine learning researchers moving from the technology sector
- Engineers with experience building research platforms and data infrastructure
The strongest opportunities are likely to sit at the intersection of technical knowledge, research discipline, sound judgement and market understanding.
The next phase of development is likely to focus less on the initial adoption of machine learning and more on how generative AI can automate research workflows, increase research throughput and support better decision-making.
As AI takes on more implementation and testing work, quantitative researchers may spend less time on routine tasks and more time generating hypotheses, directing research and deciding which ideas are worth pursuing.
Candidates can explore current quantitative analytics, research and trading roles to see where firms are hiring and the skills they require.
Through her work at Selby Jennings, Hilal supports hedge funds, proprietary trading firms and quantitative investment managers across Europe that are hiring for machine learning-focused quantitative research roles.
Candidates interested in applying machine learning and generative AI within quantitative finance can contact Hilal for a confidential discussion by completing the form below.