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- Tasks: Drive data science tools and implement advanced analytics solutions for customer success.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact by leveraging data to solve complex business challenges.
- Qualifications: Experience in data analysis, machine learning, and strong programming skills required.
- Other info: Dynamic team environment with a focus on continuous learning and development.
The predicted salary is between 36000 - 60000 £ per year.
Join us as an 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 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:
- Understand the requirements and needs of our business stakeholders.
- Develop good relationships with them, form hypotheses, and identify suitable data and analytics solutions to meet their needs and achieve our business strategy.
- Maintain and develop 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.
- Proactively bring together statistical, mathematical, machine-learning and software engineering skills to consider multiple solutions, techniques, and algorithms.
- Implement ethically sound models end-to-end and apply software engineering and a product development lens to complex business problems.
- Work with and lead both direct reports and wider teams in an Agile way within multi-disciplinary data to achieve agreed project and Scrum outcomes.
- Use your data translation skills to work closely with business stakeholders to define business questions, problems or opportunities that can be supported through advanced analytics.
- Select, build, train, and test complex machine models, considering model valuation, model risk, governance, and ethics throughout to implement and scale models.
The skills you’ll need:
- Evidence of project implementation and work experience gained in a data-analysis-related field as part of a multi-disciplinary team.
- An undergraduate or a master’s degree in a quantitative discipline, or evidence of equivalent practical experience.
- Experience with statistical software, database languages, big data technologies, cloud environments and machine learning on large data sets.
- Demonstrated leadership, self-direction and a willingness to both teach others and learn new techniques.
- Proficient in key AWS tools including EMR, Airflow, DynamoDB, S3, RDS, ElasticBeanStalk, EC2, 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 incidents and support service management.
- Experience in model deployment, fine-tuning, inference optimization.
- Experience in model versioning, drift detection, pipelines such as MLflow and Kubeflow.
AI Ops Engineer employer: NatWest Group
Contact Detail:
NatWest Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Ops Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, visualisations, and any machine learning models you've built. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in AI and data science. Be ready to discuss how you've tackled complex problems and how you can bring value to their team.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are excited about joining us. Tailor your application to highlight how your experience aligns with the role of AI Ops Engineer.
We think you need these skills to ace AI Ops Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Ops Engineer role. Highlight your experience with data science tools and methods, and don’t forget to mention any relevant projects you've worked on. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science and how you can contribute to our team. Be sure to mention specific examples of how you've tackled complex problems in the past.
Showcase Your Technical Skills: We’re keen on seeing your technical prowess! Make sure to list your experience with AWS tools, programming languages, and any big data technologies you’ve worked with. This will help us understand your fit for the role right away.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at NatWest Group
✨Know Your Data Science Tools
Make sure you’re well-versed in the key AWS tools mentioned in the job description, like EMR, S3, and Lambda. Brush up on your knowledge of machine learning frameworks and be ready to discuss how you've used them in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex data issues. Think about times when you debugged problems using tools like Splunk or CloudWatch, and be ready to explain your thought process and the outcomes.
✨Understand Business Needs
Demonstrate your ability to translate business requirements into data solutions. Be prepared to discuss how you’ve built relationships with stakeholders and how you’ve identified opportunities for advanced analytics to drive business strategy.
✨Stay Current with Trends
Show that you’re proactive about keeping up with emerging trends in data science. Share insights on recent developments or tools you’ve explored, and how they could potentially benefit the company’s objectives.
