Enterprise Architect Lead, Data Science & AI Full Time

NatWest Group

Join us as an Enterprise Architect Lead, Data Science & AI

• Join a team that is shaping the future of the bank’s data science and AI architecture and governance and help us unlock the full potential of our data.
• You’ll be defining the intentional architecture for your assigned scope in order to ensure that the current architecture being delivered by engineers best supports the enterprise and its long-term strategy.
• With valuable exposure, you’ll be building and leveraging relationships with colleagues across the bank to ensure commercially focused decisions and to create long-term value for the bank.

What you’ll do

We’re embarking on a new and exciting phase of the bank’s transformation agenda, where data science and AI architecture play a crucial role in achieving our mission to simplify, reduce risk, and enable customer engagement and personalisation. Supporting the development of architectures and roadmaps of innovative data solutions, leveraging data science and AI, is critical to driving simplification, improving data efficiency, and ensuring data risk stays within tolerance.

In this key role, you’ll be defining and communicating the current, resultant, and target state architecture for your enterprise solutions using data science & AI. You’ll also ensure that the architecture links to, and is informed by, our overall strategy and architecture, and produces the architecture outcomes.

We’ll look to you to influence the development of business strategies at an organisational level, identifying transformational opportunities for our businesses and technology areas associated with both new and existing technologies. You’ll manage and mentor a team of specialist and senior data architects dedicated to supporting architectures for solutions using data science and AI, fostering a collaborative, high-performance culture that encourages innovation.

As well as this, you’ll:

• Translate architecture roadmaps into packages of work that allow frequent incremental delivery of value to be included in the product backlog.
• Define, create and maintain architecture models, roadmaps, standards, and outcomes, using architecture strategies to ensure alignment to adjacent and higher-level models.
• Work closely with business owners, portfolio managers, product managers, and release managers to define the target intentional architecture.
• Lead complex and technically challenging architectural transformations, coordinating design and platform teams across domains.
• Stay abreast of the commercial and emerging data technology offerings that may influence current and target Data Science and AI architectures and their governance.
• Seek out and utilise continuous feedback, fostering adaptive design and engineering practices to drive the collaboration of programmes and teams around a common technical vision.

The skills you’ll need

We’re looking for someone with a background in defining business and application architectures and roadmaps for data science and AI-based solutions. You’ll bring expert knowledge of enterprise data architecture, solution data architecture, and architecture for data science and AI, along with knowledge of industry architecture frameworks such as TOGAF or ArchiMate.

In addition, you’ll be a thought leader of architecture for data science and AI solutions and enterprise design in the bank, influencing business and technology teams. You’ll have excellent communication skills with the ability to clearly communicate complex technical concepts to colleagues up to senior leadership level, along with a good understanding of Agile methodologies and experience of working in an Agile team.

You’ll also have:

• Experience of developing, syndicating, and communicating architectures, designs, and proposals for action.
• Experience of working with business solution vendors, technology vendors, and products within the market.
• Experience as an enterprise architect with a focus on architecture advisory, along with experience in AI or ML and Data Science solutions and patterns.
• Experience with developing data exploration, visualisation, and statistical analysis technologies.
• An understanding of neural networking, deep learning, and LLM architectures.
• Knowledge of Data Science lifecycle, MLOps, model management, and automation.
• Knowledge of data architecture frameworks such as DAMA and DCAM, as well as data-related regulations such as GDPR, CCPA, PCI DSS, and BCBS 239.
• Architecture knowledge of Tableau, AWS data stack, SageMaker, Python, EMR or Spark, Kafka, and Snowflake.