Principal Forward Deployed Data Scientist

Barclays; Whippany (NJ); On-site; USD 235,000 - 285,000; Full time

Job summary

Barclays is seeking a Senior Forward Deployed Data Scientist in Whippany, NJ. This very senior, hands-on technical leadership role will drive production AI from concept to deployment within a regulated environment, partnering with stakeholders to understand problems and deliver agentic AI solutions.

You will own the lifecycle of AI systems, from design to deployment, ensuring reliability and governance while staying ahead of rapid changes in AI technology.

Qualifications

• Building AI/LLM applications in production — real systems people rely on.
• Taking ideas from prototype to production — move from PoC to hardened system.
• Proven track record delivering production software in regulated environments.

Responsibilities

• Take ideas from prototype to production, proving quickly with a prototype and then engineering for production-grade reliability.
• Build agentic AI end-to-end, deploying autonomous and tool-using AI systems for internal and customer-facing use.
• Design agentic architectures and orchestration systems, including multi-agent setups, tool calling, MCP, and safety mechanisms.

Skills

AI/LLM production

Prototype to production

Regulated environment

Agentic AI

AI authentication

Software engineering fundamentals

Stakeholder communication

Forward-deployed mindset

Tools

Model Context Protocol (MCP)

Job description

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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, observable, testable, maintainable systems.
• Excellent communication and stakeholder skills — fluent moving between a business conversation and a technical design, and comfortable working directly with non-technical stakeholders.
• A forward-deployed mindset — comfortable with ambiguity, close to the business and the user, biased toward shipping and iterating.

STRONGLY DESIRABLE

• Retrieval-augmented generation (RAG) — grounding responses in trusted organisational knowledge.
• Evaluation (evals) — defining, measuring, and improving the quality and reliability of AI and agent behaviour.
• Guardrails and safety — input/output filtering, hallucination mitigation, control over autonomous actions, and responsible-AI practices.
• Prompt and context design, memory, and other techniques for shaping reliable model and agent behaviour.
• Choosing the right approach for the problem — range and judgement to select between different models, agent patterns, and architectures as the problem demands.
• Leading architecture across teams — setting technical direction that other engineers build on.
• An insatiable pull toward new and emerging technology — you keep up with the frontier because you want to, and you can tell production-ready from hype.

NICE TO HAVE

• Experience building customer-facing AI — delivering AI systems that real external customers interact with, and handling the extra bar for safety, trust, and polish that comes with it.
• Exposure to agent-driven and AI-generated UI — interfaces that agents render, drive, or adapt dynamically.

All Senior Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.

Minimum Salary:$235,000

Maximum Salary:$285,000

The minimum and maximum salary/rate information above includes only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.