At a Glance
- Tasks: Build AI/ML infrastructure and tools to enhance productivity in trading.
- Company: Join DRW, a dynamic multi-asset trading firm with a collaborative culture.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Exciting career development opportunities in a rapidly evolving industry.
- Why this job: Make an impact by solving real business challenges with cutting-edge AI technology.
- Qualifications: Experience in AI/ML and a passion for collaboration in fast-paced environments.
The predicted salary is between 60000 - 80000 £ per year.
DRW is seeking an AI Engineer to join a collaborative multi-asset trading team.
You will build infrastructure and tooling that leverages AI/ML to enhance productivity and tackle challenging business problems.
You will work with traders, researchers and data scientists to deploy and monitor production ML models, optimize resources, and evolve scalable AI platforms in a fast-paced environment.
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AI Engineer: Build Production ML Pipelines & Tools in London employer: P2P
P2P is an exceptional employer that fosters a dynamic work culture focused on innovation and collaboration in the heart of Greater London. With a commitment to employee growth, we offer extensive professional development opportunities and a supportive environment for those passionate about advancing decentralized finance. Join us to be part of a forward-thinking team that values your contributions and encourages meaningful impact in the financial services sector.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer: Build Production ML Pipelines & Tools in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like P2P!
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like P2P.
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We think you need these skills to ace AI Engineer: Build Production ML Pipelines & Tools in London
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 P2P, 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 P2P. 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 P2P
✨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.
