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- Tasks: Join our prediction team to enhance predictive models and tackle sports analytics challenges.
- Company: Be part of a dynamic team focused on innovative data solutions in the sports industry.
- Benefits: Enjoy generous leave, competitive pension, health perks, gym subsidies, and learning budgets.
- Why this job: Work collaboratively with data scientists while exploring your own ideas in a fun environment.
- Qualifications: 3+ years in predictive modeling, strong Python skills, and familiarity with advanced analytics techniques.
- Other info: Engage in regular sports activities with colleagues for a balanced work-life experience.
The predicted salary is between 36000 - 60000 £ per year.
My client is seeking a Quantitative Analyst to join their prediction team. You will use extensive datasets to enhance predictive models, explore new methodologies, and develop solutions. This role involves a blend of in-depth analysis and innovative modelling to tackle unique challenges in sports analytics. You will be joining a collaborative team of data scientists and sports analysts, as well as having the freedom to explore and develop individual ideas.
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3+ years of experience in predictive modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries.
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Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting.
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Strong programming skills, particularly in Python.
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Experience in exploring new datasets, identifying data quality issues, and handling imperfect data effectively.
Preferred Qualifications
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Understanding of expected value and utility principles.
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Practical problem-solving approach, balancing detail with quick delivery of MVPs.
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Ability to independently deliver projects and make informed decisions
What you’ll get
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Generous annual leave (including bank holidays)
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Competitive pension contributions
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Health and well-being benefits
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Subsidised gym membership and meals
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Learning and development budgets
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Regular sports activities with colleagues
Harrington Starr Quantitative Analyst - Football employer: Harrington Starr
Contact Detail:
Harrington Starr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Harrington Starr Quantitative Analyst - Football
✨Tip Number 1
Familiarize yourself with the specific predictive modeling techniques mentioned in the job description, such as Monte Carlo simulation and Bayesian modeling. Being able to discuss these methodologies in detail during your interview will demonstrate your expertise and enthusiasm for the role.
✨Tip Number 2
Showcase your programming skills in Python by working on personal projects or contributing to open-source projects related to sports analytics. This hands-on experience will not only enhance your skills but also provide you with concrete examples to discuss during interviews.
✨Tip Number 3
Network with professionals in the sports analytics field through platforms like LinkedIn or relevant forums. Engaging with others in the industry can lead to valuable insights and potential referrals that could help you land the job.
✨Tip Number 4
Prepare to discuss how you've handled imperfect data in past projects. Providing specific examples of how you identified data quality issues and developed solutions will highlight your problem-solving skills and practical approach, which are crucial for this role.
We think you need these skills to ace Harrington Starr Quantitative Analyst - Football
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description and understand the key responsibilities and qualifications required for the Quantitative Analyst position. Tailor your application to highlight relevant experience in predictive modelling and sports analytics.
Highlight Relevant Experience: In your CV and cover letter, emphasize your 3+ years of experience in predictive modelling, machine learning, and probability theory. Provide specific examples of projects or roles where you utilized these skills, especially in the sports or gaming industries.
Showcase Technical Skills: Clearly outline your programming skills, particularly in Python. Mention any relevant techniques you are familiar with, such as Monte Carlo simulation or Bayesian modelling, and provide examples of how you've applied them in past projects.
Demonstrate Problem-Solving Abilities: Illustrate your practical problem-solving approach in your application. Discuss how you balance detail with quick delivery of MVPs and provide examples of how you've independently delivered projects and made informed decisions in previous roles.
How to prepare for a job interview at Harrington Starr
✨Showcase Your Technical Skills
Be prepared to discuss your experience with predictive modeling and machine learning. Highlight specific projects where you've used techniques like Monte Carlo simulation or Bayesian modeling, and be ready to explain your thought process and the outcomes.
✨Demonstrate Problem-Solving Abilities
Prepare examples that showcase your practical problem-solving approach. Discuss how you've balanced detail with quick delivery in past projects, especially in sports analytics or related fields.
✨Discuss Data Quality Management
Since handling imperfect data is crucial for this role, be ready to talk about your experience in identifying data quality issues. Share specific instances where you successfully managed and improved data sets.
✨Emphasize Collaboration and Independence
This position involves working within a collaborative team while also having the freedom to explore individual ideas. Prepare to discuss how you've successfully collaborated with others and delivered independent projects in the past.
