Job Description
Join us as a AI Ops Engineer
In this role, you’ll drive and embed the design and implementation of data science tools and methods, which harness our data to drive market-leading purpose customer solutions
Day-to-day, you’ll act as a subject matter expert and articulate advanced data and analytics opportunities, bringing them to life through data visualisation
If you’re ready for a new challenge, and are interested in identifying opportunities to support external customers by using your data science expertise, this could be the role for you
What you’ll do
We’re looking for someone to understand the requirements and needs of our business stakeholders. You’ll develop good relationships with them, form hypotheses, and identify suitable data and analytics solutions to meet their needs and to achieve our business strategy.
You’ll be maintaining and developing external curiosity around new and emerging trends within data science, keeping up to date with emerging trends and tooling and sharing updates within and outside of the team.
You’ll also be responsible for:
Proactively bringing together statistical, mathematical, machine-learning and software engineering skills to consider multiple solutions, techniques, and algorithms
Implementing ethically sound models end-to-end and applying software engineering and a product development lens to complex business problems
Working with and leading both direct reports and wider teams in an Agile way within multi-disciplinary data to achieve agreed project and Scrum outcomes
Using your data translation skills to work closely with business stakeholders to define business questions, problems or opportunities that can be supported through advanced analytics
Selecting, building, training, and testing complex machine models, considering model valuation, model risk, governance, and ethics throughout to implement and scale models
The skills you’ll need
To be successful in this role, you’ll need evidence of project implementation and work experience gained in a data-analysis-related field as part of a multi-disciplinary team. We’ll also expect you to hold an undergraduate or a master’s degree in a quantitative discipline, or evidence of equivalent practical experience.
You’ll also need experience with statistical software, database languages, big data technologies, cloud environments and machine learning on large data sets. And we’ll look to you to bring the ability to demonstrate leadership, self-direction and a willingness to both teach others and learn new techniques.
Additionally, you’ll need:
Proficient in key AWS tools including EMR, Airflow, DynamoDB, S3, RDS, ElsacticBeanStalk, EC2,EMR, Kinesis, Lambda and CloudWatch
Experience using Java, Scala , Spark, Python , shell,, pyspark along with experience in DevOps Tools like Git , bitbucket , Jenkins and Artifactory
Strong ability to debug complex data issues from splunk , spark and cloudwatch audit logs, working closely with stakeholders to manage customer incident and support Service management .
Experience in model deployment, fine-tuning, inference optimization.
Experience model versioning, drift detection, pipelines such as MLflow and Kubeflow
In this role, you’ll drive and embed the design and implementation of data science tools and methods, which harness our data to drive market-leading purpose customer solutions
Day-to-day, you’ll act as a subject matter expert and articulate advanced data and analytics opportunities, bringing them to life through data visualisation
If you’re ready for a new challenge, and are interested in identifying opportunities to support external customers by using your data science expertise, this could be the role for you
What you’ll do
We’re looking for someone to understand the requirements and needs of our business stakeholders. You’ll develop good relationships with them, form hypotheses, and identify suitable data and analytics solutions to meet their needs and to achieve our business strategy.
You’ll be maintaining and developing external curiosity around new and emerging trends within data science, keeping up to date with emerging trends and tooling and sharing updates within and outside of the team.
You’ll also be responsible for:
Proactively bringing together statistical, mathematical, machine-learning and software engineering skills to consider multiple solutions, techniques, and algorithms
Implementing ethically sound models end-to-end and applying software engineering and a product development lens to complex business problems
Working with and leading both direct reports and wider teams in an Agile way within multi-disciplinary data to achieve agreed project and Scrum outcomes
Using your data translation skills to work closely with business stakeholders to define business questions, problems or opportunities that can be supported through advanced analytics
Selecting, building, training, and testing complex machine models, considering model valuation, model risk, governance, and ethics throughout to implement and scale models
The skills you’ll need
To be successful in this role, you’ll need evidence of project implementation and work experience gained in a data-analysis-related field as part of a multi-disciplinary team. We’ll also expect you to hold an undergraduate or a master’s degree in a quantitative discipline, or evidence of equivalent practical experience.
You’ll also need experience with statistical software, database languages, big data technologies, cloud environments and machine learning on large data sets. And we’ll look to you to bring the ability to demonstrate leadership, self-direction and a willingness to both teach others and learn new techniques.
Additionally, you’ll need:
Proficient in key AWS tools including EMR, Airflow, DynamoDB, S3, RDS, ElsacticBeanStalk, EC2,EMR, Kinesis, Lambda and CloudWatch
Experience using Java, Scala , Spark, Python , shell,, pyspark along with experience in DevOps Tools like Git , bitbucket , Jenkins and Artifactory
Strong ability to debug complex data issues from splunk , spark and cloudwatch audit logs, working closely with stakeholders to manage customer incident and support Service management .
Experience in model deployment, fine-tuning, inference optimization.
Experience model versioning, drift detection, pipelines such as MLflow and Kubeflow
💡 Quick Summary
Seeking a career-building opportunity? The AI Ops Engineer position is now open for candidates interested in the Bank Jobs sector. This role in Edinburgh offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Bank Jobs is a plus.
