Senior Machine Learning Researcher

Longshot Systems Ltd; Greater London; Hybrid; GBP 45,000 - 65,000; Full time

Job summary

A leading sports analytics company in London is looking for a Machine Learning Researcher to enhance their predictive modeling capabilities. You will design, test, and implement machine learning models in Python, with significant autonomy to explore innovative solutions. Ideal candidates will have a strong quantitative background, preferably with a Master's or PhD, and experience in competitive modeling projects. Benefits include a bonus scheme, matched pension contributions, and private healthcare.

Benefits

Participation in uncapped bonus scheme

10% matched pension contributions

Private healthcare insurance

Long term illness insurance

Gym membership

Choose your own hardware

Qualifications

• At least one of a Masters or PhD in Machine Learning, Maths, or Physics from a top university.
• Significant Kaggle experience or similar competitive modelling projects.
• Industry experience in Quantitative Research or other competitive industries.

Responsibilities

• Design and implement new machine learning models in Python.
• Improve existing models based on historical event data.
• Participate in the complete R&D process from design to implementation.

Skills

Python programming

Machine Learning frameworks

Tabular data modelling

Education

Masters or PhD in a quantitative subject

Job description

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At Longshot Systems we're building advanced platforms for sports betting analytics and trading.

We're hiring Machine Learning Researchers for our quantitative modelling team. The primary goal of this team is to improve the predictive power of our models based on historical event and market data. The quality of our models is incredibly important to us and improvements on our models directly impact company success.

You will design, test, and implement new machine learning models in Python, continually improving our existing state-of-the-art solutions. Longshot is a small, focused company and so the role suits someone who wants to be involved in all aspects of the R&D process, from high-level design through to production implementation.

The ideal candidate will be highly creative and enjoy generating new, innovative ways to tackle problems and suggesting improvements to existing methodologies; you'll have a high level of autonomy to research whichever methods you felt would be best suited to the problem at hand. A strong mathematical understanding of the fundamentals of Machine Learning and core statistics is very important for this role. Sports betting knowledge isn't required, though experience modelling sports - especially in-play football, basketball, or tennis - is helpful.

We are a hybrid working company, working Thursdays in our London (Farringdon) office and flexible the rest of the week. Our typical working hours are 10 am to 6 pm UK time, Monday to Friday, but we support flexible working and trust our team to manage their own schedules to meet their goals.

Our interview process is as follows:

• Intro call (30 mins) - your background + interests
• Technical interview (60 mins) - modelling questions + coding exercise
• Full assessment day (10:30-5pm) - a full day modelling exercise & meet the team

Requirements

At least one of:

• Masters or PhD in a quantitative, technical subject (e.g. Machine Learning, Maths, Physics) from a top university
• Significant Kaggle experience (especially in tabular data) or similar competitive modelling projects or competitions
• Industry experience in Quantitative Research (especially for sports) or other industries with competitive modelling requirements

Experience with

• Python programming
• A range of Machine Learning software frameworks
• Tabular data modelling

Benefits

• Participation in the uncapped company bonus scheme, typically 20-30% of salary depending on experience
• 10% matched pension contributions
• Private healthcare insurance
• Long term illness insurance
• Gym membership
• Choose your own hardware & setup for your development environment