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Open nowPosted today

Principal Forward Deployed Data Scientist

barclays78 open roles

Where
Building 300 Whippany Campus Jefferson Park
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Your applicationOpen nowPrincipal Forward Deployed Data Scientistbarclays · Building 300 Whippany Campus Jefferson Park

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The clock on this job

Early applications get read.

10.5% of postings close within 7 days. Measured by our own scanner across the market. barclays postings stay open a median of 6 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.3%3 days
  3. 10.5%7 days
  4. 15.9%14 days
  5. 34.1%30 days
This job: posted today

barclays median: 6 days open

The posting

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, 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.

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