Enterprise Architect Lead, Data Science
6 days ago
Join us asan Enterprise Architect Lead, Data Science &AI
- Join a team thatis shaping the future of the bank’s data science and AIarchitecture and governance and help us unlock the full potentialof our data
- You'll be defining the intentionalarchitecture for your assigned scope in order to ensure thatthe current architecture being delivered by engineers best supportsthe enterprise and its long term strategy
- Withvaluable exposure, you’ll be building and leveraging relationshipswith colleagues across the bank to ensure commercially focuseddecisions and to create long term value for thebank
We’re embarking on a newand exciting phase of the bank’s transformation agenda, datascience and AI architecture play a crucial role in achieving ourmission to simplify, reduce risk and enable customer engagement andpersonalisation. Supporting the development of architectures androadmaps of innovative data solutions, leveraging data science andAI, is critical to driving simplification, improving dataefficiency, and ensuring data risk stays withintolerance.
In this key role,you’ll be defining and communicating the current, resultant andtarget state architecture for your enterprise solutions using datascience & AI. You’ll also be making sure that the architecturelinks to, and is informed by, our overall strategy andarchitecture, and produces the architectureoutcomes.
We’ll look to you toinfluence the development of business strategies at anorganisational level, identifying transformational opportunitiesfor our businesses and technology areas associated with both newand existing technologies. You’ll manage and mentor a team ofspecialist and senior data architects dedicated to supportingarchitectures for solutions using data science and AI fostering acollaborative, high performance culture that encouragesinnovation.
As well as this,you’ll:
- Translatearchitecture roadmaps into packages of work that allow frequentincremental delivery of value to be included in productbacklog
- Define, create and maintainarchitecture models, roadmaps, standards and outcomes, usingarchitecture strategies to ensure alignment to adjacent and higherlevel model
- Work closely with business owners,portfolio managers, product managers and release managers to definethe target intentional architecture
- Leadcomplex and technically challenging architectural transformations,coordinating design and platform teams acrossdomains
- Stay abreast of the commercial andemerging data technology offerings that may influence current andtarget Data Science and AI architectures and theirgovernance
- Seek out and utilise continuousfeedback, fostering adaptive design and engineering practices todrive the collaboration of programmes and teams around a commontechnicalvision
We’re looking forsomeone with a background in defining business and applicationarchitectures and roadmaps for data science and AI based solutions.You’ll bring expert knowledge of enterprise data architecture,solution data architecture, architecture for data science and AIwith knowledge of industry architecture frameworks such as TOGAF orArchiMate.
In addition,you’ll be a thought leader of architecture for data science and AIsolutions and enterprise design in the bank influencing businessand technology teams. You’ll have excellent communication skillswith the ability to clearly communicate complex technical conceptsto colleagues up to senior leadership level, along with a goodunderstanding of Agile methodologies with experience of working inan Agile team.
You’ll alsohave:
- Experience ofdeveloping, syndicating and communicating architectures, designsand proposals for action
- Experience of workingwith business solution vendors, technology vendors and productswithin the market
- Experience as an enterprisearchitect with focus on architecture advisory along with experiencein AI or ML and Data Science solutions andpatterns
- Experience with developing, dataexploration, visualisation and statistical analysistechnologies
- Experience with developing, dataexploration, visualisation and statistical analysistechnologies
- An understanding of neuralnetworking, deep learning and LLMarchitectures
- Knowledge of Data Sciencelifecycle, MLOps, model management andautomation
- Knowledge of data, architectureframeworks such as DAMA and DCAM as well as data relatedregulations such as GDPR, CCPA,PCI DSS and BCBS239
- Architecture knowledge of Tableau, AWS datastack, SageMaker, Python, EMR or Spark, Kafka andSnowflake
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