Data Science​/Applied AI Lead

Overview

Applied AI at First Citizens is not about fitting every problem to the nearest large language model, nor about chasing the latest foundation model release. It is about understanding a meaningful business problem well enough to know what kind of solution it actually calls for - which might be a predictive model, a decision tree, a classical statistical method, a natural language processing approach, a generative AI solution, or no AI t last option is not a failure;

it is good judgment.

Not every problem needs AI. Not every AI problem needs GenAI. Holding that standard - and being willing to say so - is central to how this team earns credibility and delivers durable value. Success here is measured by fit‑for‑purpose solutions, disciplined evaluation, responsible implementation, and outcomes that the bank can actually sustain and stand behind.

The Applied AI / Data Science Lead will provide hands‑on execution capacity across data science and generative AI engineering. The role works closely with business product owners, data and technology teams, AI platform partners, and Responsible AI and risk stakeholders to shape use cases, build solutions, establish evaluation methods, and support the path from experimentation to production. This is a senior professional individual contributor role – someone who can independently lead complex technical work, make sound modeling and design decisions, and communicate trade‑offs clearly to stakeholders at multiple levels.

Business Problem Framing and Use Case Shaping

• Work with business leaders and product owners to identify, assess, and shape high‑value data science and AI opportunities across the General Bank, Commercial Bank, and Enterprise Functions.
• Translate business questions into well‑defined analytical problem statements, with clear success measures, data requirements, solution hypotheses, implementation considerations, and expected value outcomes.
• Assess whether a problem is best addressed through conventional analytics, statistical modeling, machine learning, generative AI, workflow change, or no AI solution at all – and recommend a fit‑for‑purpose approach grounded in evidence and practicality, not novelty.
• Support prioritization of AI use cases by evaluating business value, data readiness, implementation feasibility, risk and control implications, operating model requirements, and the ability to measure impact over time.

Solution Design and Hands‑On Development

• Design, build, validate, and refine analytical and AI solutions using appropriate methods: predictive modeling, supervised and unsupervised machine learning, natural language processing, generative AI, retrieval‑augmented generation, optimization, or other advanced analytics techniques – selected on the basis of fit, not fashion.
• Develop data pipelines, features, model prototypes, prompt or retrieval configurations, evaluation datasets, reusable code assets, and supporting documentation required for experimentation and responsible implementation.
• Establish transparent baselines and, where warranted, challenger approaches so that solution complexity is justified by measurable performance improvement or business value – not technical preference alone.
• Contribute technical judgment on model selection, vendor capabilities, enterprise platform services, solution architecture, integration needs, and production‑readiness considerations.

Evaluation, Measurement, and Responsible Delivery

• Define and execute fit‑for‑purpose evaluation plans covering model performance, stability, interpretability, robustness, data quality, user acceptance, operational feasibility, monitoring, and business outcome measurement as appropriate to each use case.
• For generative AI solutions, develop evaluation approaches for task accuracy, groundedness and faithfulness, retrieval quality, human review effectiveness, harmful output risk, prompt handling, and other use‑case‑specific performance and control requirements.
• Partner with Responsible AI, model risk, business risk, compliance, legal, cybersecurity, privacy, and other stakeholders to ensure solutions are developed with appropriate documentation, testing…