Accountabilities:

Take ownership of the development, maintenance, and continuous improvement of Counter-Strike 2 pre-match prediction models and their underlying analytical libraries.
Develop and optimise predictive models covering matches, individual player performance, and team-level statistics using advanced data science and statistical techniques.
Analyse premium esports datasets to identify meaningful patterns, competitive trends, player behaviours, and strategic factors that can improve prediction accuracy.
Act as the analytical bridge between data scientists and Counter-Strike 2 experts, translating practical game knowledge and feedback into actionable data features and model improvements.
Collaborate with game experts to evaluate match conditions, team strategies, player performance, and in-game situations, ensuring that predictive models reflect the realities of professional competition.
Design, develop, and maintain internal dashboards and visualisation tools that enable specialists to explore player statistics, team performance trends, and changes in the competitive meta.
Monitor model performance, identify opportunities for improvement, and refine analytical methodologies based on new data and expert feedback.
Explore and integrate AI-powered tools, workflows, and autonomous agents into daily processes to improve research, analysis, development efficiency, and overall productivity.
Build expertise in pre-match prediction systems before progressively expanding responsibilities towards the development and optimisation of live prediction models.
Communicate analytical findings and technical concepts clearly to non-technical colleagues, supporting informed decision-making across the team.

Requirements

Expert-level knowledge of the Counter-Strike 2 competitive scene, including professional teams, players, tournament dynamics, game strategies, and evolving competitive trends.
Practical experience with Python programming, particularly for data science, statistical analysis, and predictive modelling.
Strong foundations in statistics, mathematics, and machine learning, with the ability to apply quantitative methods to complex competitive scenarios.
Understanding of how in-game factors, player performance, team dynamics, and strategic decisions influence match outcomes.
Strong analytical and problem-solving skills, with the ability to interpret complex datasets and translate findings into meaningful predictive insights.
Excellent communication skills, including the ability to explain technical concepts and analytical results to colleagues without a technical background.
Proactive and organised working style, with a strong sense of ownership and the ability to independently manage responsibilities and deliver results.
Enthusiasm for artificial intelligence and a willingness to experiment with AI-powered tools as part of everyday work.

Additional experience that would be particularly valuable includes:

Previous experience developing predictive models for Counter-Strike 2, other esports titles, or traditional sports.
Practical experience using AI tools in professional workflows, including building custom AI-powered processes, automation systems, or autonomous agents.
Familiarity with rating systems and player or team ranking methodologies.
Experience developing data visualisations, analytical dashboards, and interactive reporting tools.
Knowledge of Valorant or other competitive esports ecosystems.
Previous exposure to esports betting, odds modelling, or sports analytics.

Benefits

Remote working arrangement within Europe.
Freelance contract offering flexibility and professional independence.
Opportunity to combine professional Counter-Strike 2 expertise with advanced data science and predictive modelling.
Access to premium esports datasets to support sophisticated research and model development.
Opportunity to work alongside experienced data scientists, quantitative specialists, and competitive gaming experts.
Significant ownership over the development and continuous improvement of pre-match prediction models.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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