# Principal Forward Deployed Data Scientist at Barclays

Barclays is hiring a Principal Forward Deployed Data Scientist. It pays $235k a year and Level rates it Builds AI ●●●●; you can [apply on Level](https://jobsbylevel.com/go/ebb93fa2-5b1a-4ceb-89a5-65cc69fd9201?utm_medium=ai-agent).

AI Level 4, AI centrality 87 out of 100. Building 300-Whippany Campus, Jefferson Park.

## Details

- Company: [Barclays](https://jobsbylevel.com/companies/barclays)
- AI level: AI Level 4 (score 87 out of 100)
- Location: Building 300-Whippany Campus, Jefferson Park
- Salary: $235k
- Posted: October 16, 2026
- Apply: https://jobsbylevel.com/go/ebb93fa2-5b1a-4ceb-89a5-65cc69fd9201?utm_medium=ai-agent

## Prepare for this job

The job page lists 3 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/principal-forward-deployed-data-scientist-at-barclays-784c0f

## Description

Job Description Principal Forward Deployed Data Scientist This is a very senior, hands-on technical leadership role for a data scientist who has built real, production AI applications for internal teams and for customers and knows what it takes to take an idea all the way from prototype to production within a regulated environment. You'll sit at the intersection of our business, our users, and the bleeding edge of AI. You won't be handed a spec you'll work directly with stakeholders to understand the problem, choose the right approach for it, build the solution, deploy it, and stay with it as it grows. You are the person who turns ambiguous goals into production agentic systems that people depend on, and who keeps us ahead of a technology landscape that changes week to week. WHAT YOU'LL DO • Take ideas from prototype to production. Start from a rough concept or business problem, prove it quickly with a prototype, then do the hard engineering to turn it into a reliable, scalable, production-grade system people can depend on — and know what each stage demands. • Build agentic AI, end to end. Design, implement, deploy, and evolve autonomous and tool-using AI systems — agents that reason, plan, call tools, and act on the user's behalf — for both internal and customer-facing use, owning the full lifecycle and the outcome. • Design agentic architectures. Build agent-orchestration systems: single and multi-agent setups, tool and function calling, Model Context Protocol (MCP) and agent-to-agent coordination, planning and reasoning loops, memory, identity and access for agents, and the control and safety mechanisms that keep autonomous behaviour trustworthy. • Match the solution to the problem. No one-size-fits-all. You assess each problem on its own terms and choose the right approach — a different model, agent pattern, or architecture for a different need — rather than forcing everything through a single template. • Live on the bleeding edge. Actively track new models, agentic frameworks, techniques, and tooling as they emerge; prototype quickly, form a view, and bring the ones that matter into production with sound judgement about what's ready and what's hype. • Deliver, dependably. Ship software to production on time and to a high standard, navigating the controls, governance, and compliance expectations of a regulated environment. • Embed with the business. Work shoulder-to-shoulder with stakeholders, shape the roadmap, and translate business goals into working AI capability. • Set the standard. Define how agentic AI is built here — quality, evaluation, observability, and responsible-AI practices that others follow — and be the trusted technical authority for AI delivery across the organisation. ESSENTIAL (MUST-HAVE) • Building AI/LLM applications in production — real systems people rely on, whether internal or customer-facing, not prototypes or demos. • Taking ideas from prototype to production — a proven ability to move from a fast proof of concept to a hardened, scalable, maintainable production system, and the judgement to know what changes between the two. • A proven track record of delivering software to production in a regulated environment — real, shipped systems with lasting impact, delivered within governance, risk, compliance, and audit controls (e.g. financial services or similar) without losing delivery pace. • Hands-on experience with agentic AI — building systems where models reason, plan, call tools, and take actions, including multi-agent and agent-to-agent (A2A) coordination, tool and data integration via the Model Context Protocol (MCP), and the control and safety mechanisms that keep autonomous behaviour trustworthy. • Identity and access for AI — how agents authenticate, act on behalf of users with delegated authority, and operate under non-human/service identities, with the authentication, authorisation, and auditability a regulated environment demands. • Strong software engineering fundamentals — reliable,

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/principal-forward-deployed-data-scientist-at-barclays-784c0f

Source: https://jobsbylevel.com/jobs/principal-forward-deployed-data-scientist-at-barclays-784c0f

## Cite this page

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