Machine Learning Researcher

Posted 2 Days Ago
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London, Greater London, England
Junior
Software
The Role
As a Machine Learning Researcher at Longshot Systems, you will enhance predictive models for sports betting analytics by designing, testing, and implementing machine learning models using Python. Collaboration with senior leadership in a hybrid work environment allows for high autonomy and creativity in problem-solving related to model improvement.
Summary Generated by Built In

Description

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

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

You will work closely with the CEO, CTO, and Team Lead to 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 and ideally you'll have experience in doing research on cutting-edge models either in industry or academia. Knowledge of sports betting or horse racing, which this team focuses on, isn’t required.

We are a hybrid working company, working Thursdays in our London (Farringdon) office and remotely 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. We are able to sponsor candidates who need a Visa.

We're open to applicants across a range of experience levels applying to this role, from Juniors up to Seniors. Our interview process is as follows:

  1. A 60 minute technical interview with our CEO and/or Team Lead, discussing your previous experience and also discussing some modelling scenarios and how you'd approach them
  2. An assessment day, lasting from 9:30am until 5pm, where you'll be tackling a real modelling problem using data very similar to what we use in practise
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 or other industries with competitive modelling requirements

Experience with:

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

Our salary range for the role is £70,000 to £180,000, depending on experience (Juniors at the bottom of the range, Seniors towards the top) and interview performance.

List of benefits:

  • Participation in the uncapped company bonus scheme, typically 10-30% 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.

Top Skills

Python
The Company
HQ: London
14 Employees
On-site Workplace
Year Founded: 2016

What We Do

Longshot Systems is a small startup producing high throughput, low latency trading software and tools for use in sports betting markets.

Our core systems handle thousands of trading signals per second, all of which must be processed and potentially acted upon with minimal latency. We have similar challenges to high frequency trading shops, but in the sports betting world.

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