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- Tasks: Develop and improve machine learning models for soccer data analytics.
- Company: Join Swish Analytics, a cutting-edge sports analytics startup.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Why this job: Make a real impact in the exciting world of sports betting and analytics.
- Qualifications: Masters in Data Science or related field with experience in sports analytics.
- Other info: Collaborative team culture with a focus on innovation and technical excellence.
The predicted salary is between 36000 - 60000 £ per year.
Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We are looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.
Swish Analytics is hiring Soccer Data Scientists to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products. We are hiring a Data Scientist to support our Sports Data Models.
Duties:
- Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products.
- Develop contextualized feature sets using specific domain knowledge in soccer.
- Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models.
- Strive to constantly improve model performance using insights from rigorous offline and online experimentation.
- Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts.
- Adhere to software engineering best practices and contribute to shared code repositories.
- Document modeling work and present to stakeholders and other technical and non-technical partners.
Requirements:
- Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area.
- Demonstrated experience developing models at production scale for soccer or sports betting.
- Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods.
- Minimum of 3+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting.
- Experience with relational SQL & Python.
- Experience with source control tools such as GitHub and related CI/CD processes.
- Experience working in AWS environments.
- Proven track record of strong leadership skills. Has shown ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions.
- Excellent communication skills to both technical and non-technical audiences.
Soccer Data Scientist - Europe employer: Swish Analytics
Contact Detail:
Swish Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Soccer Data Scientist - Europe
✨Tip Number 1
Network like a pro! Reach out to folks in the sports analytics field, especially those at Swish Analytics. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning models or any relevant projects. This is your chance to demonstrate your expertise in soccer data science beyond just words on a CV.
✨Tip Number 3
Prepare for the interview by brushing up on your knowledge of soccer analytics and predictive modelling. Be ready to discuss your past experiences and how they relate to the role at Swish. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Swish Analytics.
We think you need these skills to ace Soccer Data Scientist - Europe
Some tips for your application 🫡
Show Your Passion for Soccer: When writing your application, let us know why you're passionate about soccer and how it drives your work in data science. We love seeing candidates who can connect their personal interests with their professional skills!
Highlight Relevant Experience: Make sure to showcase your experience in developing machine learning models, especially in the context of sports or betting. Use specific examples that demonstrate your expertise and how you've tackled challenges in this area.
Be Clear and Concise: We appreciate clarity! Keep your application straightforward and to the point. Avoid jargon unless it's necessary, and make sure your key achievements stand out so we can easily see what you bring to the table.
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at Swish Analytics!
How to prepare for a job interview at Swish Analytics
✨Know Your Soccer Stats
Make sure you brush up on your soccer statistics and data models. Be ready to discuss how you've applied machine learning and statistical methods in your previous roles, especially in relation to soccer or sports betting. This will show your passion and expertise in the field.
✨Showcase Your Technical Skills
Prepare to demonstrate your proficiency in Python, SQL, and any relevant tools like GitHub. You might be asked to solve a problem on the spot, so practice coding challenges beforehand. Being able to articulate your thought process while coding can really impress the interviewers.
✨Communicate Clearly
Since you'll be working with both technical and non-technical teams, practice explaining complex concepts in simple terms. Think of examples where you've successfully communicated your ideas to diverse audiences. This will highlight your strong communication skills, which are crucial for this role.
✨Be Ready for Problem-Solving Scenarios
Expect to tackle some real-world problems during the interview. Prepare by thinking through how you would approach model development and performance improvement. Discussing your problem-solving strategies will demonstrate your ability to think critically and adapt in a fast-paced environment.