LSports
We’re searching for a

Data Scientist

R&DRamat Gan, IL

About The Position

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.
  • 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.
  •  Mentor junior team members in data science best practices, including the application of statistical concepts.
  • 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**

 

Requirements

  • 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 - Must. Ph.D - Advantage.
  • Proficiency in programming languages commonly used in data science such as Python or R.
  •  Understanding of Machine Learning, its advantages and challenges.
  •  Strong experience in machine learning algorithms and frameworks such as scikit-learn, TensorFlow, or PyTorch.
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