Description

LSports is the leading global provider of sports data, dedicated to revolutionizing the industry through innovative solutions. We excel in sports data collection and analysis, advanced data management, and cutting-edge services like AI-based sports tips and high-quality sports visualization. As the sports data industry continues to grow, LSports remains at the forefront, delivering real-time solutions. 

If you share our love of sports and tech, you've got the passion and will to better the sports-tech and data industries - join the team! We are looking for a highly motivated Machine Learning Engineer.

About the team: Trade360

Trade360 group is the powerhouse behind LSports' exceptional portfolio of customer-facing products, blending innovation, functionality, and user experience to deliver industry-leading solutions that redefine customer engagement and satisfaction.

Responsibilities:

• Lead the design and implementation of advanced machine learning algorithms to solve complex problems in a customer facing environment.
• Collaborate closely with data engineers, product managers and domain experts to operationalize machine learning models into production.
• Evaluate the effectiveness of machine learning models through comprehensive validation metrics and make iterative improvements.
• Conduct research to keep abreast of the latest advancements in machine learning and artificial intelligence.

Requirements

• 5+ years of experience in machine learning or data science, with a proven track record of deploying machine learning models into production.
• Proficiency in Python and machine learning frameworks like PyTorch, or scikit-learn.
• Strong understanding of algorithms, data structures, and statistical modeling techniques.
• Product oriented - proven experience with ML PoCs / MVP from ideation to execution.
• Experience with SQL and NoSQL Databases.
• Experience with Databricks or similar.

Advantages:

• MLOps experience
• Proven experience with Recommendation Systems
• Experience with Kafka
• Working in an early-stage startup environment