Staff Forward Deployed AI Quant

Staff Forward Deployed AI Quant - Investment Bank Research AI

Overview

The Investment Bank is entering the agentic AI era, redefining how research is generated and consumed. To accelerate this shift, the organisation is building a new quantitative AI capability within its Research division. As a Staff Forward Deployed AI Quant, you will apply advanced modelling, ML/AI techniques, and agentic intelligence to modernise research workflows - bringing quantitative rigour, rapid experimentation, and analytical innovation directly to front‑line researchers.

This role is ideal for quantitative thinkers who want to explore the frontier of LLMs, multi‑agent systems, modern ML pipelines, and data‑driven research augmentation.

Role Purpose

In this role, you will work directly with Research teams to identify high‑impact opportunities where quantitative modelling and AI can reshape how insights are discovered, generated, and distributed. You will design and prototype new quantitative approaches, push the boundaries of agentic intelligence, and help define the Research division's next generation of analytical tools.

Rather than focusing on infrastructure or heavy engineering, your work centres on modelling, experimentation, analytical frameworks, and quantitative AI methodology.

Key Responsibilities

1. Quantitative Prototyping & Algorithmic Exploration

• Build rapid PoCs grounded in statistical reasoning, ML modelling, and agentic AI behaviour.
• Evaluate concepts using quantitative validation frameworks, experiment design, and measurable KPIs.
• Prototype tools that enhance analyst workflows through modelling, forecasting, summarisation, or dynamic reasoning.

2. Quant‑Driven Collaboration with Research

• Partner with analysts to understand data, domain assumptions, and research methodologies.
• Identify opportunities to inject quantitative techniques - e.g., modelling, pattern detection, signal extraction, generative reasoning.
• Translate qualitative research processes into structured, quant‑friendly workflows suitable for AI augmentation.

3. Quantitative Architecture & Methodology Design

• Shape analytical architectures for ML/LLM‑driven PoCs, focusing on modelling pipelines rather than infrastructure.
• Design workflows that combine retrieval, agentic reasoning, statistical modelling, and generative components.
• Document modelling logic, assumptions, evaluation strategies, and experimental setups.

4. Agentic AI, ML, and Modelling Techniques

• Build agentic systems using LLMs, multi‑agent reasoning, tool‑calling, and context‑aware decision processes.
• Implement ML methods relevant to research, such as classification, ranking, summarisation, recommendation, or pattern recognition.
• Explore new quantitative techniques for research automation and insight generation.

5. Applied Quantitative Work with Data

• Work hands‑on with datasets used in research (macroeconomic, market, thematic, textual).
• Apply quantitative analysis to uncover structure, relationships, and potential AI‑assisted enhancements.
• Use AWS tools primarily as analytical enablers - data retrieval, model execution, and experimental pipelines.

6. Stakeholder Engagement

• Communicate modelling concepts clearly to stakeholders with varying levels of technical understanding.
• Collaborate iteratively with researchers, incorporating domain knowledge into quant designs.
• Present prototypes and insights in a compelling, data‑driven manner.

7. Model Evaluation, Diagnostics & Explainability

• Apply statistical frameworks to evaluate prototype performance, reliability, and robustness.
• Conduct root‑cause analysis on modelling behaviours, agentic decisions, and output variance.
• Ensure that quant prototypes have clear rationale, interpretability, and traceability.

8. Thought Leadership in Quantitative AI

• Shape the Research division's approach to modelling, agentic AI, and quantitative augmentation.
• Identify new modelling techniques, frameworks, and agent architectures worth exploring.
• Drive best practices for experiment‑driven development and quant‑centric innovation.

Required Skills & Experience

Quantitative & Modelling Expertise

1. Strong Python from a quantitative computing perspective (data, modelling, algorithmic experimentation).
2. Proficiency in an OOP language (Java/C++) understood through the lens of modelling and system logic rather than infra.
3. Experience building ML models, LLM‑based systems, or agentic workflows.
4. Ability to design quantitative prototypes based on domain understanding and hypothesis‑driven development.
5. Familiarity with ML/AI architectures, evaluation techniques, and model lifecycle.
6. Strong analytical, diagnostic, and problem‑solving capability.
7. Experience working with bespoke research datasets or analytical environments.

Interpersonal & Analytical Communication

• Ability to translate research needs into quantitative frameworks.
• Skilled at explaining modelling behaviour to non‑technical stakeholders.
• Collaborative, curious, and comfortable with ambiguous, open‑ended problems.

Nice‑to‑Have Skills

1. Background or exposure to financial research, macro, thematic analysis, or alpha‑related modelling.
2. Knowledge of Data Mesh concepts from a data‑as‑an‑asset perspective.
3. Experience with multi‑agent frameworks and advanced LLM workflows.
4. AWS certifications with a modelling focus (ML Specialty, Data Engineering).
5. Understanding of gRPC or modern system communication standards.
6. Familiarity with container environments primarily as execution platforms for models.

Who This Role Is Ideal For

Someone who:

• Thinks deeply about models, data, evaluation, and analytical frameworks.
• Wants to explore the frontier of agentic AI applied to research.
• Enjoys rapid prototyping grounded in quantitative methods.
• Likes turning messy research workflows into structured, model‑friendly processes.
• Brings curiosity, rigour, and a quantitative mindset to AI development.

This is a role for quants who want to help define how AI transforms investment research in the agentic era.