Data Scientist

LSports is a global leader in sports data, committed to revolutionizing the industry with innovative solutions.

Our focus is on sports data collection and analysis, advanced data management, and cutting-edge services such as AI-powered sports tips and high-quality sports visualization. As the sports data landscape grows, we remain at the forefront, delivering real-time solutions.

If you're passionate about both sports and technology and want to drive the sports-tech and data industries into the future, we invite you to join our team.

About the team: Data Integrity

At LSports, we place a lot of emphasis on Data Integrity, which is a cornerstone of our long-term strategy.

Data Integrity group is at the forefront of real-time analytics, using machine learning and artificial intelligence to balance low latency and impeccable data accuracy.

Responsibilities:

• Lead the development and deployment of advanced analytical models to solve complex problems, leveraging advanced statistical techniques.
• Design and develop NLP models for tasks such as Named Entity Recognition, Classification, Text Similarity, Clustering and other tasks.
• Collaborate with cross-functional teams, including engineers and product managers, to understand business challenges and design data-driven solutions.
• Analyze large and complex data sets, applying mathematical theories and statistical methods, to produce actionable insights.
• Stay current with emerging trends in data science and statistics and evaluate their applicability to business problems.
• It is possible to work from the offices in Ramat Gan and Ashkelon**
  
  

• Minimum of 5 years of experience in data science or as a statistician, with a strong focus on mathematical modeling and statistical analysis.
• M.Sc. in Mathematics, Statistics, or a related field - An advantage.
• Proficiency in Python.
• Experience in Designing and implementing NLP models - A Must
• Experience implementing NLP models from scratch or fine tuning existing products to organizational needs
• Basic understanding in deep learning - A Must
• Understanding of Machine Learning, its advantages and challenges.
• Strong experience in machine learning algorithms and frameworks such as scikit-learn, TensorFlow, or PyTorch.