Staff Machine Learning Engineer, MTS (m/f/d) at Sportradar

ABOUT US:  As a Staff Machine Learning Engineer in Sportradar's AI unit, you will work within an established, high performing team of experts on the design, development and deployment of predictive models and algorithms powering Spotradar’s products.  You will leverage Sportradar's extensive sports datasets, betting transaction data and user interaction data collected through Sportradar's products and services reaching millions of end-users across the globe. The position will be focused on the development of AI and advanced analytics solutions for sportsbook risk management and liquidity driven odds trading, collaborating closely with product owners and technical leads in our Managed Trading Services and Odds Services product domains. Sportradar is a global leader in understanding and leveraging the power of sports data for hundreds of business customers around the world, in turn entertaining millions of sports enthusiasts. THE CHALLENGE: • Analyse and explore data using statistical analysis tools and scripting. • Develop machine learning or statistical models for use in our sportsbook risk management and odds trading products and services. • Rigorously validate methods, models, algorithms and hypotheses using back-testing on historical data and/or simulations. • Bring models into production leveraging our AI platform, maintaining ownership of the AI solution throughout the model development lifecycle, from modelling to production deployment, monitoring and continuous model improvement. • Design and implement novel methods for data analysis, tailored to our sports-related application domains and propose new ways of using data to improve our products and services. • Present ideas and solutions to software developers and business stakeholders in a clear and understandable way.   ABOUT YOU:  • The successful candidate will be knowledgeable in machine learning and statistics. • Strong programming skills and software development experience needed to productize low latency streaming analytics solutions are a must. • Experience with risk management and portfolio optimization in financial, gaming or sports betting contexts is a plus. • Experience in data science, statistical modelling, applied mathematics and/or software engineering. • Profficiency with advanced querying of large analytical databases using SQL. • Programming experience in Java and/or a similar object oriented languages is a must. • Experience with Python, R or similar statistical and/or machine learning software/toolkits. • Bonus: Hands-on experience with real-time streaming analytics frameworks such as Apache Flink and streaming technologies such as Apache Kafka. • Bonus: Experience working with Mac/Linux environments. • Bachelor of Science in Mathematics / Statistics, Computer Science / Engineering, or related field; equivalent experience acceptable. • Fluent in English (written and spoken). • Autonomous, rigorous, creative and a team player.  OUR OFFER:  • A collaborative environment with colleagues from all over the world (Engineering offices in Europe, Asia and US) including various social events and teambuilding.  • Flexibility to manage your workday and tasks with autonomy.  • A balance of structure and autonomy to tackle your daily tasks.  • Vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants.  • Global Employee Assistance Programme.  • Calm and Reulay app (leading well-being apps designed to support focus, quality rest, mindfulness, and long-term mental resilience).  • Online training videos.  • Flexible working hours. 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 an office-first business operating on a hybrid model, with team members working in the office three days a week to build relationships, exchange ideas, and grow together.    OUR RECRUITMENT PROCESS:   • Initial Screening: A quick chat with our Talent Acquisition Partner to understand your background and expectations.  • Technical Assessment: A short task to showcase your technical skills.  • Technical Interview: Meet with the Technical team and Hiring Manager to dive into your solution, as also discuss team fit.  • Onsite Interview: Meet with the local team and take a tour of our office for a final meet-and-greet.  • Finals Steps: Receive feedback and, if successful, an offer!