Data Scientist - Level 1

Hawk-Eye Innovations
Charing Cross, United Kingdom
7 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Junior
Compensation
£ 48K

Job location

Charing Cross, United Kingdom

Tech stack

Data analysis
C++
Databases
Data Files
Data Visualization
Python
Machine Learning
Rust
Data Processing
PyTorch
Pandas
Matplotlib
Scikit Learn
Information Technology
Plotly
Data Pipelines

Job description

  • 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.

Requirements

Do you have experience in Rust (programming language)?, Do you have a Bachelor's degree?, * 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.

Bonus Skills:

  • 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.

If you are enthusiastic about sports and data science and are looking for an exciting opportunity to grow your skills and make a meaningful impact in the sports industry, we would love to hear from you!

Benefits & conditions

  • 25 days annual leave (excluding bank holidays)
  • Enhanced pension scheme with 5% matching
  • Hybrid working model
  • Complimentary Unmind wellbeing app
  • Sony Group Company discounts

About the company

Hawk-Eye Innovations is a leading provider of sports technology solutions, dedicated to enhancing the accuracy and efficiency of officiating, coaching, and fan engagement across a variety of sports. We are seeking a talented and motivated Data Scientist with a strong passion for sports and analytics to join our team. The ideal candidate will possess a keen interest in sports, a solid foundation in data science, and the ability to derive insights from complex data sets.

Apply for this position