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CricViz has established itself as a market leader in the collection, analysis and dissemination of data across the world’s leading cricket competitions, with the largest and most sophisticated database in world cricket. Our work spans several verticals, including Performance Analysis, Broadcast and Media, and Fantasy and Gaming platforms. Customers include the BBC, Sky, Sony, ECB, and IPL-winning RCB.
Job Description
We are seeking a highly skilled Data Scientist to join the CricViz team. This pivotal role focuses on the end-to-end development of proprietary metrics and predictive models derived from the world’s most sophisticated cricket databases. You will lead the transformation of complex, high-frequency tracking data into market-leading, commercial-grade analytical products. Your work will empower sophisticated professional clients by surfacing match state insights and player performance indicators not captured by conventional analysis. The ideal candidate is an experienced Data Scientist/Quantitative Analyst with strong problem-solving skills, capable of working professionally within an evolving and fast-paced environment.
Key Responsibilities
- Metric Innovation: Research and develop new, high-value derived metrics from raw ball and player tracking data, such as pitch behavioural characteristics and advanced fielding analysis.
- Bespoke Client Collaboration: Act as the lead technical point of contact for private clients, working to develop custom predictive models and niche datasets tailored to their specific analytical requirements.
- Productisation of Data: Translate raw tracking data into productionised Data Science models that provide a clear commercial advantage.
- Model Assurance & Validation: Oversee rigorous back testing and optimisation cycles to ensure all models maintain the high level of accuracy and reliability required for professional-grade execution.
- Cross Functional Integration: Work closely with Engineering and Product teams to ensure seamless delivery of these complex feeds into client workflows, while maintaining the integrity of our internal IP.
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Key Requirements
- Technical Expertise: A minimum of 3 years experience in data science, with a proven track record of handling large-scale, high-granularity sports datasets and feeds.
- Predictive Modeling: Deep proficiency with the PyData stack (pandas, numpy, scikit-learn, XGBoost) and advanced machine learning methods including neural networks and random forests.
- Analytical Domain Knowledge: A professional-level understanding of cricket match dynamics and the factors influencing match state outcomes.
Nice-to-haves
- Tracking Data Experience: Prior experience working with high-frequency tracking or GPS data to create aggregated, value-add insights.
- Strategic Communication: Ability to translate highly technical modeling concepts into clear commercial value propositions for sophisticated stakeholders.
- Discretion & IP Management: Experience working in sensitive commercial environments where protecting proprietary modeling work is paramount
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Equality and Diversity
CricViz is committed to building an open and inclusive culture that supports personal development and learning. Ellipse believes in the principle of equal opportunity in employment and its employment policies for recruitment, training, development and promotion despite any differences based on individual grounds of race, colour, nationality, religion or belief, sex, sexual orientation, marital status, age, ethnic and national origin, disability or gender reassignment.
Benefits
- 25 days holiday (plus bank holidays)
- Hybrid working with regular time spent at our offices in London
- Company pension scheme
- Eye Test Contribution
- Life Insurance
- Training and Development Opportunities
About Ellipse
CricViz is part of Ellipse, a leading sports data and analytics company comprising CricViz, FootballViz, Horse Racing, RugbyViz (Oval and Stuart Farmer Media Services), and TennisViz. Working with the world’s biggest broadcasters, professional teams, and rights holders, we simplify complex data to engage a broad and diverse audience and tell better stories about the sports we love.
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