Quantitative ML Developer β€” Relocation & Growth

Quantitative ML Developer β€” Relocation & Growth

Full-Time 63000 - 77000 Β£ / year (est.) On-site
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At a Glance

  • Tasks: Build innovative statistical models in equities and commodities while collaborating with diverse teams.
  • Company: Newton Colmore, a forward-thinking firm in London focused on machine learning.
  • Benefits: Career progression, relocation support, and opportunities to enhance your skills.
  • Other info: Dynamic work environment with plenty of growth opportunities.
  • Why this job: Become an ML expert and make a real impact in a competitive industry.
  • Qualifications: Experience in machine learning and a passion for quantitative research.

The predicted salary is between 63000 - 77000 Β£ per year.

Newton Colmore in London is seeking a Machine Learning Developer focused on quantitative strategies and research.

You will build new statistical models across equities and commodities and collaborate with cross-functional partners to deliver robust solutions that outperform the competition.

You will become the ML authority, attending events and delivering presentations while advancing your skills in a forward-thinking environment with opportunities for career progression and relocation support.

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Quantitative ML Developer β€” Relocation & Growth employer: Newton Colmore

Newton Colmore is an exceptional employer, fostering a collaborative and innovative work culture where every team member's input is valued. Located in a dynamic environment, the company offers tailored benefits packages, monetary bonuses, and ample opportunities for professional growth, making it an ideal place for engineers passionate about pushing the boundaries of technology in high-performance computing.

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Contact Details:

Newton Colmore Recruitment Team

StudySmarter Expert Advice🀫

We think this is how you could land Quantitative ML Developer β€” Relocation & Growth

✨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 Newton Colmore!

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Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Quantitative ML Developer β€” Relocation & Growth at Newton Colmore.

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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 Newton Colmore.

✨Apply Directly through Our Website

When you find a suitable opening like Quantitative ML Developer β€” Relocation & Growth at Newton Colmore, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Quantitative ML Developer β€” Relocation & Growth

Machine Learning
Statistical Modelling
Quantitative Analysis
Equities Knowledge
Commodities Knowledge
Collaboration Skills
Presentation Skills

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 Newton Colmore, 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 Newton Colmore. 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 Newton Colmore

✨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 Newton Colmore!

✨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.