At a Glance
- Tasks: Support AI development by creating evaluation criteria and methodologies for risk management tools.
- Company: Join Spinwell Ltd, a forward-thinking company at the forefront of AI technology.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Be part of a dynamic team with exciting career advancement opportunities.
- Why this job: Make a real impact in AI quality and risk management while collaborating with diverse teams.
- Qualifications: Experience in AI evaluation and strong communication skills are essential.
The predicted salary is between 63000 - 77000 £ per year.
Spinwell Ltd is seeking an AI Evaluation Engineer PSAI Tech to embed with business and product teams, supporting the development and deployment of risk-management toolkits. You will translate business requirements into measurable evaluation criteria and design evaluation methodologies with QA processes across downstream applications.
The role requires defining performance metrics, assessing model quality and communicating findings to both technical and non-technical stakeholders.
AI Evaluation Engineer — AI Quality & Risk (Hybrid) employer: Spinwell Global
Spinwell Global is an exceptional employer, offering a dynamic work environment in London that promotes a hybrid working model, allowing for flexibility and work-life balance. With a strong focus on employee development, the company provides ample opportunities for growth and training, ensuring that team leaders are well-equipped to support their caseworkers effectively. The collaborative culture fosters meaningful engagement with public sector operations, making it a rewarding place to contribute to impactful community initiatives.
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We think this is how you could land AI Evaluation Engineer — AI Quality & Risk (Hybrid)
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We think you need these skills to ace AI Evaluation Engineer — AI Quality & Risk (Hybrid)
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!
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Craft a Tailored Cover Letter:For a full-time role at Spinwell Global, 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 Spinwell Global. 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 Spinwell Global
✨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.
