At a Glance
- Tasks: Engineer AI solutions to enhance data usage in Markets Sales Technology.
- Company: Join Barclays, a leading financial institution driving innovation.
- Benefits: Attractive salary, flexible working options, and career development opportunities.
- Other info: Collaborative environment with a focus on innovation and growth.
- Why this job: Be at the forefront of AI technology and make a real impact in finance.
- Qualifications: Experience in AI, ML, and data analytics is essential.
The predicted salary is between 80000 - 98000 £ per year.
Barclays, in collaboration with hackajob, seeks a Senior Applied AI Engineer for Markets Sales Technology in London. You will help make better use of data across the Markets business through analytics, ML, and AI solutions, partnering with business, technology, and governance teams to deliver data products and insights that support outcomes, risk oversight, and efficiency.
You will engineer AI-enabled capabilities that connect models, data, APIs, workflows and user actions into safe, scalable solutions.
Senior AI Engineer, Markets Tech — Scalable Data & AI employer: hackajob
At hackajob, we pride ourselves on being an exceptional employer that fosters a culture of innovation and inclusivity. Our diverse team thrives in a high-performance environment where your contributions directly impact our multi-asset platform's success. With ample opportunities for professional growth and a commitment to employee development, joining us means being part of a forward-thinking company that values your expertise and ambition.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI Engineer, Markets Tech — Scalable Data & AI
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Senior AI Engineer, Markets Tech — Scalable Data & AI
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at hackajob, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at hackajob. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at hackajob
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
