About the role

Data Scientist developing sports analytics models and algorithms for decision-making in sports at Hawk-Eye Innovations. Join a team dedicated to enhancing officiating, coaching, and fan engagement.

Responsibilities

Develop and implement sports analytics models and algorithms to support decision-making for teams, coaches, and officials across various sports.
Analyse large and complex data sets to identify trends, patterns, and insights that can be translated into actionable strategies for performance improvements.
Collaborate with cross-functional teams, including software engineers, product managers, and other data scientists, to develop and deploy data-driven solutions.
Create visualisations and reports to communicate insights and findings effectively to technical and non-technical stakeholders.
Assist in the development and maintenance of internal databases, ensuring data quality and accuracy.
Contribute to the enhancement of Hawk-Eye's proprietary analytics platforms by continuously refining and optimising their performance and user experience.
Present findings and insights to clients, partners, and internal teams, ensuring they understand the value and implications of the analytics work being performed.
Participate in the development and delivery of training materials and workshops to help clients and internal team members better understand and utilize sports analytics tools and techniques.
Actively contribute to the continuous improvement of Hawk-Eye's analytics processes and methodologies, sharing knowledge and expertise with team members to foster a culture of learning and collaboration.

Bachelor’s degree or equivalent in Data Science, Mathematics, Physical Sciences, Biomechanics, Computer Science or a similar related field.
Knowledge of sports rules, strategies, and basic statistical concepts.
Proficiency in Python and experience with data manipulation (e.g. pandas, polars) and visualization tools (e.g. plotly, matplotlib).
Strong communication and presentation skills.
Passion for sports and ideally sports analytics, with a desire to continuously learn and stay up-to-date with industry developments.
Experience with sports data is ideal but not essential.
Familiarity with sports performance and/or biomechanical data analysis.
Familiarity with machine learning frameworks and libraries, such as scikit-learn and PyTorch.
Knowledge of general purpose programming languages such as C++ or Rust.
Any experience working with large, complex data sets and managing data pipelines, ensuring data quality and integrity.
Experience in data analysis, predictive modelling, or machine learning, including academic or placement/internship experience.