Barclays Finance/FinTech

Principal Forward Deployed Data Scientist

New York In person Full-time
  • Location: Building 300-Whippany Campus, Jefferson Park
  • 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.

_

Additional Locations: New York, 43 West 23rd Street

Job description provided by Barclays.
View the original posting