Data Scientist - Physical Data / Predictions - Internship

SkillCorner collects sports data 100% automatically from a single camera feed. The company, now a global leader in its field of expertise, develops Computer Vision and Machine Learning algorithms that can detect all the moving objects (each player, the ball and the referee) in the image, locate them on the pitch, track them frame by frame, and recognize them. From this raw tracking data, SkillCorner produces Performance indicators, Game Intelligence metrics, and Visualizations that are used by clubs, national federations, and player agencies.

SkillCorner covers more than 150 competitions (both Women’s and Men’s), holds over 100 billion data points on more than 100 000 professional players. Trusted by more than 250 football, basketball and American Football teams worldwide, redefining their performance analysis, scouting, and recruitment workflows.

Team Description

The prediction team delivers objective and standardized physical performance metrics to assist in profiling, identifying, and benchmarking players, teams, and leagues globally. These metrics encompass physical data such as distance covered in speed zones, peak velocity, and acceleration profiles, as well as in-depth performance insights and context regarding both on-ball and off-ball actions.

The physical data come from a fully automated, consistent, and scalable data collection process using single-camera video, overcoming player visibility limitations. This enables objective measurement of each player’s actions, making it easy to compare and evaluate player capabilities across several leagues.

We are looking for an intern to join the Physical Data team within the Prediction group. The internship will focus on developing new data-driven approaches to better understand and predict player physical performance and progression.

The project may explore several research directions, including:

• Modeling player physical progression/development over time.
• Design of simplified and robust data quality frameworks.
• Prediction of performance adaptation when moving between competitions or leagues, based on physical demands and intensity profiles.
• Integration of body pose information to enrich physical event detection and performance metrics.

The intern will contribute across the full pipeline: data exploration, metric design, modeling, validation, and experimentation, with a strong focus on practical impact and real-world deployment.

This internship offers hands-on experience at the intersection of sports science, computer vision, and applied data science, working on research topics that directly translate into production tools used by professional organizations.

Preferred experience

• Graduate degree in engineering, computer science, mathematics or related field.
• Prior experience in Machine Learning competitions or data-driven projects is a must (e.g., Kaggle competitions or personal projects on GitHub).
• Strong knowledge of optimization problems and signal processing.
• Proficient in using Python usage for machine learning applications.
• Excellent communication skills in English and in French.
• Interest in solving real-world problems: you love connecting and fine-tuning solutions to solve hard problems.
• Great interpersonal skills and a strong team-oriented mindset.