What is the opportunity?

Reporting to the Data Science Lead, you will work closely with business and technology teams across WME to support the ongoing provision of machine learning models and insights to the business. The majority of the role focuses on building and maintaining MLOps pipelines and associated infrastructure.

What will you do?

• To be considered a blend of data and AI “evangelist,” “data guru” and “fixer” and promote the available data/AI capabilities and expertise to technology and business unit leaders.
  
• Collaborate with varied stakeholders within the organization; particularly with data scientists, data engineers, business stakeholders, data architects, and general technology teams to refine and develop requirements for various data and AI initiatives.
  
• Architecting, creating, and maintaining MLOps pipelines (code, data, and models) and associated systems (e.g. feature store, model registry, model metadata etc.) in development, testing, and production execution environments.
  
• Implement and use innovative and modern tools, techniques, and architectures to partially or completely automate the common, repeatable, and tedious data preparation, modelling, and integration tasks.
  
• Assist with renovating the data science infrastructure to drive automation and ensure production code/machine learning systems are running smoothly and efficiently.
  
• Design, implement, and maintain a range of automated tests for data, models, and code; ranging from basic unit tests to automated model performance degradation monitoring.
  
• Build curiosity and knowledge about new AI initiatives. This includes applying data and/or domain understanding and proposing appropriate (and innovative) data preparation, modelling, and operationalization techniques.
  
• Drive to ensure the data science team adheres to best software engineering and software design principles, as well as generally maintain their own high standard of technical excellence. This includes supporting in the management of projects and code review responsibilities.
  
• Ensures that consumers use the data/models provisioned to them responsibly through data governance and compliance initiatives. ML engineers should work with data governance teams (and information stewards within these teams) and participate in vetting and promoting content created in the business and by data scientists to the curated data catalog for governed reuse.
  

What do you need to succeed?
Must-have

• Proven experience in bringing machine learning models into production.
  
• Proven experience working in cross-functional teams on data products and collaborating with business stakeholders in support of a departmental and/or multi-departmental data initiatives.
  
• Degree level (MSc or Ph D) qualifications or equivalent experience in computer science, statistics, applied mathematics, data management, information systems, information science, machine learning, or a related quantitative field.
  
• Adept in agile methodologies and capable of applying Dev Ops and MLOps principles to improve the communication, integration, reuse and automation of code across an organization.
  

Nice-to-have

• Experience working with business intelligence and analytics teams who use popular data discovery, analytics, and BI software tools like Power BI, Tableau, Qlik and others for semantic-layer-based data discovery.
  
• Exposure to hybrid deployments: Cloud and On-premise.
  

What is in it for you?

We thrive on the challenge to be our best - progressive thinking to keep growing and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• Leaders who support your development through coaching and managing opportunities.
  
• Opportunities to work with the best in the field.
  
• Ability to make a difference and lasting impact.
  
• Work in a dynamic, collaborative, progressive, and high-performing team.
  
• Flexible working and hybrid options fully supported.
  

Agency Notice

RBC Group does not accept agency resumés. Please do not forward resumés to our employees, nor any other company location. RBC Group only pay fees to agencies where they have entered into a prior agreement to do so and in any event do not pay fees related to unsolicited resumés. Please contact the Recruitment function for additional details.

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Job Skills
Big Data Management, Cloud Computing, Database Development, Data Mining, Data Warehousing (DW), ETL Processing, Group Problem Solving, Quality Management, Requirements Analysis
Additional Job Details
12 SMITHFIELD STREET:LONDON London United Kingdom 35 Full time WEALTH MANAGEMENT Regular Salaried 2024-10-17 2024-11-08