Senior Data Scientist

Créer une alerte d'emploi pour cette recherche

Scientist • Ivry-sur-Seine

Dernière mise à jour : il y a 1 jour

Description de poste

At SkillCorner, we’re transforming the way sports are analyzed and understood. From football (soccer) to basketball and American football, our mission is to empower teams and organizations with advanced, data-driven insights to make smarter decisions and gain a competitive edge.

Using broadcast (TV) video feeds from games, our AI-powered platform analyzes every aspect of play, extracting detailed performance data for every player on the field or court. We take pride in being a global leader at the intersection of AI and sports, helping teams unlock actionable insights to optimize strategies, enhance performance, and shape the future of the game.

Team Description

The Prediction Team lies at the heart of our mission to leverage combined Tracking & Event Data to generate advanced performance metrics. We develop In-Possession and Out-of-Possession metrics using cutting-edge Deep Learning and Machine Learning techniques, providing deeper insights into team and player performance.

We develop sophisticated algorithms that handle a variety of complex challenges, including predictive models such as xReceiver, xPass, xThreat. These types of model outputs help to detect and classify player-specific on-ball and off-ball actions/events. This has led to the creation of innovative dynamic events enriched with specific attributes derived from tracking and predictive values from model outputs.

Job Description

We are currently recruiting a Junior Data Scientist to work in the Prediction team. In this role, you will:

• Design, implement, and optimize cutting-edge algorithms alongside a more experienced team member. As a junior team member, you are expected to have firsthand experience in deep learning applied to Time Series Data, with a demonstrated interest in the underlying mechanisms of neural networks and prior hands-on experience in training them. Your curiosity and eagerness to grow will be strong assets for this position.
• Ensure projects conform to best practices for implementing, maintaining, and improving predictive models throughout their life cycles.
• Continually enhance your own and your colleagues’ knowledge of football/sport and data science through documentation, reading, research, and discussion with your teammates and the rest of the front office.
• Collaborate with football subject matter experts in scouting, machine learning, decision science, and more, integrating their expertise into innovative models.

This position offers a rare balance between advanced R&D and real-world application. You’ll have the freedom to explore and develop innovative solutions, knowing that your work will quickly transition from research to production, directly impacting sports teams and organizations worldwide.

Preferred Experience

• Graduate degree/PhD in engineering, computer science, mathematics or related field.
• Comfortable with Python usage in machine learning, deep learning, and/or computer vision.
• Experience in data manipulation and visualization.
• Excellent communication skills in English and in French.
• Interest in solving real-world problems: you love connecting and tweaking things to solve hard problems.
• Great interpersonal skills and a strong team-oriented mindset.
• Strong knowledge of football.

Recruitment Process

When you submit, you will have a section (in a form) to answer the following question:

We have been asked to identify the best wingers in football. What models would you build to answer that question, and how would you apply those models to decision-making? (250 word limit)

Tip: There’s no defined right or wrong answer. Responses are used to get some insight into how you approach problem solving and football in general.

Additional Process

• Optional: 15-20 minutes introduction call
• Interview - open discussion around knowledge and experiences, Q&A session on a practical scenario
• Interview - technical homework
• Optional - personal interview: motivations, expectations, conditions