At a Glance
- Tasks: Enhance predictive analytics for sports betting using machine learning.
- Company: Longshot Systems, a leader in innovative sports analytics.
- Benefits: Hybrid work model, flexible hours, and creative autonomy.
- Other info: Collaborate in our Farringdon office every Thursday for team synergy.
- Why this job: Join a dynamic team and make a real impact in sports betting.
- Qualifications: Master's or PhD in a quantitative field; Python and ML framework skills required.
The predicted salary is between 60000 - 80000 £ per year.
Longshot Systems is looking for a Machine Learning Researcher to join our quantitative modeling team focused on enhancing predictive analytics for sports betting. This hybrid position allows flexibility in work hours, with in-office collaboration on Thursdays in our Farringdon office, London.
Candidates should possess a Master's or PhD in a quantitative field and be proficient in Python and machine learning frameworks.
The role offers a creative and autonomous work environment, contributing directly to key company successes through innovative model development and enhancements.
Senior ML Researcher - Hybrid & Flexible Hours in London employer: Longshot Systems
Longshot Systems is an exceptional employer that fosters a creative and autonomous work environment, perfect for those passionate about machine learning and predictive analytics. With the flexibility of hybrid working and collaborative in-office days in the vibrant Farringdon area of London, employees enjoy a supportive culture that prioritises innovation and personal growth, making it an ideal place for talented individuals to thrive and contribute to exciting projects in the sports betting industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML Researcher - Hybrid & Flexible Hours in London
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We think you need these skills to ace Senior ML Researcher - Hybrid & Flexible Hours 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!
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Craft a Tailored Cover Letter:For a full-time role at Longshot Systems, 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 Longshot Systems. 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 Longshot Systems
✨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.
