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Data Scientist – Technology Solutions
Twenty First Group. Develop, train and evaluate models using statistical and machine learning techniques, focusing on probabilistic approaches.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and evaluating statistical and machine learning models, with a strong focus on probabilistic approaches and data pipeline management. Proficient in Python and SQL for data exploration and model development, while effectively communicating findings to diverse audiences.
Highest-signal resume keywords
Machine Learning TechniquesProbabilistic ModelsData Pipeline ManagementPython ProgrammingSQL Proficiency
ATS Keywords
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Hard Skills
Statistical TechniquesFeature EngineeringModel ValidationBayesian InferenceData ExplorationModel Training PipelineModel EvaluationMonte Carlo MethodsProbabilistic Simulation
Soft Skills
CollaborationCuriosityCommunicationClient Stakeholder Engagement
Tools & Technologies
AWS LambdaEventBridgeDynamoDBAI-Assisted Coding Tools
Industry Keywords
Sports DataB2C ApplicationsB2B ApplicationsFan-Facing Products
Tech Stack
Tools & technologiesAWSDynamoDBPythonSQL
About the role
Key responsibilities & impact- Develop, train and evaluate models using statistical and machine learning techniques, focusing on probabilistic approaches.
- Contribute across the modelling lifecycle from feature engineering and training through validation and deployment.
- Query, clean and explore datasets using Python and SQL to surface patterns and support model development.
- Help build and maintain data pipelines that ingest and validate new and often messy sports data sources.
- Leverage AI tools to accelerate and improve day-to-day workflow.
- Support solutions built around specific sporting events, including fixed deadlines and go-live support.
- Apply model development discipline through version control, testing and documentation.
- Develop models behind custom-built solutions for sports events and properties.
- Contribute to broadcast, digital and fan-facing products delivered through B2C/B2B applications and APIs.
- Work within a cross-functional squad and collaborate with colleagues across the business and disciplines.
Requirements
What you’ll need- Passion for Sport: You follow sport closely and understand the context of the data and audiences we build for. Comfortable with sport-driven modelling decisions.
- Solid grounding in machine learning, supervised and unsupervised methods, and classical statistical techniques.
- Comfortable working with probabilistic models, uncertainty estimation and Bayesian inference.
- Understanding of the full model training pipeline, including data preparation, feature selection, model selection and model validation.
- Hands-on experience building and evaluating models in a data science or quantitative context.
- Comfortable using Python and SQL for data exploration, feature development and modelling workflows.
- An appetite for the engineering side of the work — you want to understand and help own the pipeline that feeds your model.
- Ability to present findings clearly to both technical and non-technical audiences.
- Comfortable explaining your work directly to client stakeholders and translating their needs into modelling decisions.
- Curiosity about data and user behaviour.
- Ability to collaborate within a cross-functional team and contribute to shared knowledge and code bases.
- Keen to develop knowledge and skills and keep up to date with relevant developments.
- Nice to have: passion for golf and familiarity with strokes gained, shot-level data and tournament flow.
- Nice to have: experience building and maintaining production data pipelines or working with AWS Lambda, EventBridge and DynamoDB.
- Nice to have: experience with Monte Carlo methods or probabilistic simulation.
- Nice to have: familiarity with AI-assisted coding tools such as Claude Code or Cursor.
Benefits
Comp & perks- Hybrid working out of our London office (Farringdon) - most of our staff come into the office about three days a week
- Salary based on our external benchmarking framework, plus eligibility for a bonus scheme
- Private health insurance
- Personal days, including birthdays and health and wellness days
- AI forward culture