Counter-Strike 2 Expert Data Scientist

JobgetherEsports analytics

Based in GermanyContract

Salary not disclosed

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Job Details

Required Skills: PythonArtificial IntelligenceMachine LearningData scienceData visualization

Have expert-level knowledge of the Counter-Strike 2 competitive scene, including professional teams, players, tournaments, strategies, and competitive trends.
Have practical Python programming experience, particularly in data science, statistical analysis, and predictive modelling.
Have strong foundations in statistics, mathematics, and machine learning.
Understand how in-game factors, player performance, team dynamics, and strategic decisions influence match outcomes.
Be able to interpret complex datasets and translate findings into predictive insights.
Be able to explain technical concepts and analytical results to colleagues without a technical background.
Be proactive and organised, with a strong sense of ownership and ability to manage responsibilities independently.
Be enthusiastic about AI and willing to experiment with AI-powered tools.
Experience developing predictive models for Counter-Strike 2, other esports, or traditional sports is valuable.
Experience with AI tools in professional workflows, custom AI processes, automation, or autonomous agents is valuable.
Familiarity with rating systems and player or team ranking methods is valuable.
Experience developing data visualisations, analytical dashboards, or interactive reporting tools is valuable.
Knowledge of Valorant or other competitive esports ecosystems is valuable.
Exposure to esports betting, odds modelling, or sports analytics is valuable.

Develop, maintain, and continuously improve Counter-Strike 2 pre-match prediction models and analytical libraries.
Build and optimise models for matches, individual player performance, and team statistics.
Analyze esports datasets to identify patterns, competitive trends, player behaviours, and strategic factors.
Translate game knowledge and expert feedback into data features and model improvements.
Collaborate with game experts to evaluate match conditions, team strategies, player performance, and in-game situations.
Develop and maintain dashboards and visualisation tools for player statistics, team performance, and competitive trends.
Monitor model performance and refine analytical methods using new data and expert feedback.
Explore AI-powered tools, workflows, and autonomous agents to support research, analysis, and development.
Progress from pre-match prediction systems toward live prediction model development.
Explain analytical findings and technical concepts to non-technical colleagues.

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