At a Glance
- Tasks: Analyse and improve sports betting strategies while collaborating with tech experts.
- Company: Join Longshot Systems, a leader in sports betting analytics.
- Benefits: Enjoy competitive bonuses, private healthcare, gym membership, and a matched pension scheme.
- Other info: Flexible hybrid working model with excellent career growth opportunities.
- Why this job: Combine your passion for sports with data analysis to make a real impact.
- Qualifications: Strong knowledge of sports betting and proficiency in Python data analysis.
The predicted salary is between 40000 - 50000 £ per year.
At Longshot Systems we build advanced platforms for sports betting analytics and trading. We're looking for a Sports Betting Trading Analyst to come and join our modelling team where you will be one of our sports & betting market experts. You'd be joining a small team and working closely alongside software engineers and machine learning experts to provide the domain expert insight in improving the trading strategies we develop for our clients.
Your role is to identify trading strategy improvements and value leaks in existing and new strategies through a combination of watching our client's trading activity from our internal tools, data analysis on historical trading and intuition from your sport and betting market knowledge.
You should have a deep interest in sports and sports betting, especially from the professional sports bettor perspective. Ideally you have strong working knowledge of betting exchanges, prediction markets, sharp bookmakers (including the US and Asian markets) and general betting market dynamics. You're a general sports fanatic and enjoy following a wide variety of sports, especially thinking about them from a sports betting angle. You're comfortable with Jupyter notebooks and data analysis in Python. You're numerate and good with numbers, ideally with a degree of core statistical knowledge and understanding.
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. This role especially benefits from working during sporting events, although ultimately it's up to you to decide what's the most valuable use of time and we have excellent tools to re-look at games in normal hours.
Interview Process
- Intro call (30 mins) - your background + interests
- 1st Technical interview (90 mins) - a deeper dive into your experience
- Full assessment day (10:00–6pm) - a one day on-site where you’ll analyse some data we provide for you using Python to generate useful insights.
Responsibilities
- Analyse our existing automated sports betting strategies and identify potential improvements in them
- Collaborate with software developers and machine learning experts to optimise and enhance trading algorithms
- Work with the team to develop brand new trading strategies on new markets and new sports
- Perform data analysis on historical trading to back up your qualitative analysis
- Conduct ongoing research on the sports betting markets
Requirements
- Required:
- Strong knowledge of sports betting markets across a variety of different sports
- A strong intuition for spotting bad trading decisions and identifying ways to improve them
- Intermediate to advanced proficiency in data analysis in Python
- Excellent problem-solving skills and ability to work with large datasets
- Strong communication in a predominately remote environment, especially written communication
- Excellent teamwork skills, with the ability to collaborate effectively with software developers and machine learning experts
- Desired:
- Previous experience in professional sports betting
- Advanced data science experience
- Experience with quantitative analysis and statistical modelling
- Familiarity with low latency and high throughput trading environments
- An interest in working with the engineers and researchers to implement your improvement ideas directly
Benefits
- Competitive bonus scheme
- 10% matched pension contributions
- Private healthcare insurance
- Long term illness insurance
- Gym membership
Sports Betting Trading Analyst in London employer: Longshot Systems
At Longshot Systems, we pride ourselves on fostering a dynamic and collaborative work environment where innovation thrives. As a Sports Betting Trading Analyst, you'll not only engage with cutting-edge technology and data analysis but also enjoy the flexibility of hybrid working in our vibrant London office. With competitive benefits, including a generous bonus scheme and private healthcare, we are committed to supporting your professional growth and ensuring you have the tools to excel in the exciting world of sports betting.
StudySmarter Expert Advice🤫
We think this is how you could land Sports Betting Trading Analyst in London
✨Tip Number 1
Get to know the company inside out! Research Longshot Systems, their products, and their culture. This will help you tailor your conversations during interviews and show that you're genuinely interested in being part of the team.
✨Tip Number 2
Network like a pro! Connect with current employees on LinkedIn or attend industry events. This can give you insider info and might even lead to a referral, which can boost your chances of landing that interview.
✨Tip Number 3
Prepare for those technical interviews by brushing up on your Python skills and data analysis techniques. Practise using Jupyter notebooks to analyse datasets, as this is likely to come up during your assessment day.
✨Tip Number 4
Don’t forget to showcase your passion for sports betting! During interviews, share your insights and experiences related to betting markets. This will demonstrate your enthusiasm and expertise, making you stand out as a candidate.
We think you need these skills to ace Sports Betting Trading Analyst in London
Some tips for your application 🫡
Show Your Passion for Sports Betting:Let us know how much you love sports and betting! Share your experiences, favourite sports, and any insights you've gained from your betting adventures. This will help us see your enthusiasm and fit for the role.
Highlight Your Data Skills:Since you'll be working with data analysis in Python, make sure to showcase your experience with Jupyter notebooks and any relevant projects. We want to see how you can turn numbers into actionable insights!
Tailor Your Application:Don’t just send a generic application! Tailor your CV and cover letter to reflect the specific skills and experiences that match our job description. This shows us you’ve done your homework and are genuinely interested in joining our team.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the process smoother for everyone involved!
How to prepare for a job interview at Longshot Systems
✨Know Your Sports Inside Out
Make sure you brush up on your sports knowledge, especially the ones relevant to the role. Be prepared to discuss recent games, player stats, and betting trends. This will show your passion and expertise in the field, which is crucial for a Sports Betting Trading Analyst.
✨Master Python and Data Analysis
Since you'll be using Python for data analysis, practice your skills beforehand. Familiarise yourself with Jupyter notebooks and ensure you can manipulate datasets effectively. You might even want to run through some sample analyses to feel confident during the technical interview.
✨Prepare for Technical Questions
Expect to dive deep into your experience during the technical interview. Review common trading strategies and be ready to discuss how you would improve them. Think about past experiences where you've identified value leaks or made successful trading decisions.
✨Show Off Your Teamwork Skills
Collaboration is key in this role, so be ready to share examples of how you've worked with others, especially in remote settings. Highlight your communication skills and how you’ve successfully collaborated with software developers or data scientists in the past.
