At a Glance
- Tasks: Build scalable AI systems for finance and optimise workflows on HPC clusters.
- Company: Join Jump Trading Group, a leader in quantitative finance technology.
- Benefits: Competitive salary, flexible work hours, and opportunities for professional growth.
- Other info: Fast-paced, collaborative environment with excellent career advancement potential.
- Why this job: Work with cutting-edge ML techniques and make an impact in the finance world.
- Qualifications: Experience in software engineering and familiarity with ML concepts.
The predicted salary is between 63000 - 77000 £ per year.
Jump Trading Group is seeking world-class engineers to collaborate with our research, trading and engineering teams to build state-of-the-art ML systems for quantitative finance.
You will work on training pipelines, low-latency inference, and production deployment.
Join a fast-paced, collaborative environment where you apply cutting-edge techniques to complex domains, optimize workflows on HPC clusters, and implement scalable ML platforms across languages including C, C++, Python, and CUDA.
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Campus ML Engineer: Build Scalable AI for Finance employer: Trading Interview
Tower Research Capital is an exceptional employer that fosters a dynamic and collaborative work culture, where innovation and rigorous experimentation are at the forefront. Located in a vibrant financial hub, employees benefit from cutting-edge technology and resources, alongside ample opportunities for professional growth and development within the fast-paced world of quantitative trading. Join us to be part of a team that values your contributions and rewards your success in a meaningful way.
StudySmarter Expert Advice🤫
We think this is how you could land Campus ML Engineer: Build Scalable AI for Finance
✨Get Involved in Data Science Meetups
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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 Trading Interview.
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We think you need these skills to ace Campus ML Engineer: Build Scalable AI for Finance
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 Trading Interview, 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 Trading Interview. 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 Trading Interview
✨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!
✨Showcase Your Projects
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
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Trading Interview!
✨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.
