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Reporting to the Head of Quants you will be working closely with your colleagues in Quants but also various product owners, developers and traders company wide. Our department is responsible for creating predictive models for both pre-match and in-play betting markets in various sports. This typically requires cleaning up, visualizing, analyzing and interpreting relevant data, as well as implementing the created models in Java and testing them.
Publishing and maintaining these Java libraries is also one of our responsibilities. Furthermore, we are providing various data analysis or generally math related tasks on demand. THE CHALLENGE - Learn how predictive models look like in a betting context and build models on your own - Support the team in maintaining the codebase - Analyzeoptimize existing predictive models as model co-owner - Research new methods for future applications
Required: - Master’s degree or equivalent/higher qualification in one of the following disciplines: Data Science, Statistics, Mathematics, Software Engineering or a closely related field - Experienced in predictive modelling, with a deep understanding of data analysis and modelling techniques such as GLMs, Time-Series Analysis, Bayesian Statistics, MCMC, neural networks, etc. - Very strong skills in R or python - Advanced coding and analytical skills - Strong communication skills, fluency in English - Creative thinking, keen to further refine systems and processes - Highly self-motivated, leading by example and enthusiasm - Being a team player, with the ability to work with new teams immediately - Strong interpersonal skills Preferred: - Experience in object-oriented programming, preferably Java - Experience with Gitlab, Jira & Confluence - Experience with SQL OUR OFFER - A collaborative environment with colleagues from all over the world (Offices in Europe, Asia, North & South America). - Impactful Work: Contribute to the development of groundbreaking products that influence industries globally. - Ability to shape your own workday and career via a clearly defined professional and personal development plan. - We are a diverse and collaborative global team with a unique spirit, determined to achieve our goals with integrity and focus. - Opportunity to work with senior leadership, develop yourself and build your career within an inspiring and fast-growing company and digital sports environment. - Flexible working model. While we appreciate the flexibility and benefits of working from home, we strongly believe that coming together in person fosters stronger connections, encourages collaboration, and drives innovation—both as individuals and as a company. The energy, shared ideas, and team support we experience in the office strengthen the foundation of our success and culture.
For this reason, we are generally an office-first business operating on a hybrid model, with team members working in the office four days a week to build relationships, exchange ideas, and grow together.
The minimum monthly salary for this position is according to the Collective Bargaining Agreement. Overpayment will be considered depending on qualifications and working experience. At Sportradar, we celebrate our diverse group of hardworking employees.
Sportradar is committed to ensuring equal access to its programs, facilities, and employment opportunities. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. We encourage you to apply even if you only meet most of the requirements (but not 100% of the listed criteria) – we believe skills evolve over time.
If you’re willing to learn and grow with us, we invite you to join our team!
We’re the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on our know-how and technology to boost their business.
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Zur OriginalanzeigeModelle bauen, trainieren und verbessern: von klassischem Machine Learning über Computer Vision bis zum Feinschliff großer Sprachmodelle.