Senior Analyst, AI Implementation

Third Bridge produces primary research for private equity, credit, and corporate clients globally. The Private Markets Strategy function owns how that content is built, deployed, and scaled. We are integrating AI across the content lifecycle — from expert identification and vetting through transcript generation, Company Briefs, and demand-signal detection. The AI Implementation team turns those initiatives into working systems.

You will work across three broad surfaces:

• Internal content and operations systems — tooling and data workflows that make our content cleaner, faster to produce, and easier to discover across the business.
• Client-facing AI products — the next generation of AI-powered research experiences delivered to institutional clients.
• Demand and coverage intelligence — systems that detect where content is thin, where client demand is concentrating, and which companies, sectors, or themes to prioritise next.

• →Own one or more workstreams end-to-end: scope the problem, design the approach, build it in Python, and carry it through to a stable, monitored production system.
• →Design and productionise prompt-based workflows, retrieval pipelines, and evaluation harnesses, with an eye on quality, cost, and reliability at scale.
• →Partner directly with Technology, Product, Compliance, and Data teams to integrate builds into the wider stack and clear the path to deployment.
• →Set engineering standards for the team — code review, testing, documentation — and coach Analysts through their work.
• →Diagnose and resolve data-quality and performance issues, and make pragmatic trade-offs when internal systems impose constraints.
• →Contribute to the team's roadmap by surfacing high-value opportunities and flagging risk early.

• Not a pure research role. The emphasis is on shipping and integrating reliable systems, not novel model development.
• Not an end-to-end infrastructure ownership role. You build in close partnership with the teams that own the underlying platforms rather than owning that infrastructure yourself.
• Not a people-management role at this level, though you will mentor Analysts and are expected to lead by technical example.

• 2–5 years of experience building and shipping software or data/ML systems in a production environment. Consulting (MBB or Big 4) or high-growth startup experience alongside solid technical delivery is a plus.
• Strong Python and a track record of taking work from prototype to production, including testing, monitoring, and iteration.
• Practical experience with LLM workflows — prompting, retrieval, and evaluation — and good judgement about where they do and do not fit.
• Ability to work independently within a workstream, make defensible trade-offs, and communicate them clearly to technical and non-technical partners.
• Comfort operating across functions and translating fuzzy business problems into concrete, shippable systems.
• Early signs of leadership — mentoring juniors, raising the bar on quality, and owning outcomes rather than tasks.

• The reliability, quality, and business impact of the systems you own end-to-end.
• Autonomy — how much can be handed to you with confidence, and how well you manage ambiguity.
• Effectiveness of your cross-functional partnerships and the strength of the Analysts you coach.

The natural progression is toward the AI Implementation Lead (AVP) track, subject to performance, sustained ownership of production systems, growing cross-functional influence, and demonstrated coaching of Analysts.