Staff Machine Learning Engineer, MTS

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 Sportradar’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. 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. Proficiency with advanced querying of large analytical databases using SQL. Programming experience in Java and/or a similar object oriented languages is a plus. 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.

Knowledgeable in machine learning and statistics, strong programming skills, software development experience, experience with risk management and portfolio optimization, experience in data science, statistical modelling, applied mathematics and/or software engineering, proficiency with SQL, programming experience in Java and/or similar object-oriented languages, experience with Python, R or similar statistical and/or machine learning software/toolkits, hands-on experience with real-time streaming analytics frameworks such as Apache Flink and streaming technologies such as Apache Kafka, experience working with Mac/Linux environments, Bachelor of Science in Mathematics / Statistics, Computer Science / Engineering, or related field; equivalent experience acceptable, fluent in English.