Analyst, AI Implementation

The AI Implementation Analyst is a hands-on, early-career technical role within the AI Implementation team inside Private Markets Strategy. Working under the AI Implementation Lead and alongside Senior Analysts, you will help build, test, and maintain the AI-powered systems that make Third Bridge's research faster to produce, cleaner, and easier to use. It is an ideal first or second role for someone who is technically strong, genuinely curious about applied machine learning and large language model (LLM) workflows, and eager to learn how AI is deployed in a fast-moving commercial environment.

You will work on real production problems from day one, not synthetic exercises. Over your first 12 to 24 months you will develop depth in one or more of the team's core workstreams and will be coached closely by the Senior Analysts and the Lead. Strong performers progress toward the Senior Analyst level.

• →Write clean, readable Python to gather, clean, and prepare data from internal sources for use in machine learning and LLM pipelines.
• →Build and iterate on prompt-based workflows, retrieval pipelines, and lightweight evaluation harnesses under the direction of a Senior Analyst or the Lead.
• →Maintain and improve existing internal tools and integrations — fixing bugs, extending functionality, and keeping systems running as requirements evolve.
• →Help move promising prototypes toward stable, repeatable production systems, in partnership with our Technology, Product, and Data teams.
• →Support diagnosis of data-quality issues and propose practical fixes within the constraints of internal systems.
• →Contribute to documentation, testing, and quality assurance across the team's active builds.

• Not a research or academic data science role. The emphasis is on deployment and integration, not novel model development.
• Not a fully autonomous role. You will work within scoped tasks defined by a Senior Analyst or the Lead, with close coaching and structured feedback.
• Not a slides or reporting role. The primary output is working code and reliable, maintained systems.

• 0–2 years of experience in a technical, analytical, or engineering role. Recent graduates in computer science, engineering, statistics, or a related quantitative field are welcome.
• Working proficiency in Python, with comfort handling data (for example pandas) and calling APIs. Exposure to LLMs or prompt engineering is a strong plus but not essential — aptitude and curiosity matter more.
• Sound fundamentals in data structures and clean coding habits, and a willingness to write tests and documentation.
• A pragmatic, problem-solving mindset: happy to work within messy real-world constraints and iterate quickly.
• Clear written and verbal communication, and the humility to ask questions early and take feedback well.
• Genuine interest in how AI creates commercial value in a research and expert-network business.

• Quality and reliability of the code and tooling you own or contribute to.
• Rate of learning — how quickly you move from supervised tasks to owning small pieces of a workstream.
• Consistency, responsiveness, and how well you collaborate with Senior Analysts and cross-functional partners.

The natural progression is to Senior Analyst, typically after 18 to 24 months in role, subject to performance, demonstrated ownership of at least one production-grade component, and evidence of independent problem-solving.