Senior Data Scientist
Role Overview
The Senior Data Scientist is a senior technical role combining deep expertise in statistics, mathematics and data preparation with strong capability in machine learning and hands-on programming. The role leads complex analyses, develops scalable solutions and ensures work is rigorous, reproducible and aligned with business needs.
Working across departments, the Senior Data Scientist communicates findings to technical and non-technical stakeholders, influences technical decisions and helps colleagues develop their skills. The role demonstrates ownership, sound business judgement, effective execution and resilience, while promoting innovation, continuous learning and responsible use of data.
Key Responsibilities
• Play a key role in data science projects from problem definition through analysis, modelling, implementation and evaluation.
• Define appropriate statistical, mathematical and machine learning approaches for complex business problems.
• Lead data exploration, cleaning, transformation and feature engineering to create reliable analytical datasets.
• Design, implement and optimise machine learning models, with appropriate validation and performance monitoring.
• Develop maintainable, production-ready Python code using sound software engineering practices.
• Present findings, limitations and recommendations clearly to technical and non-technical stakeholders.
• Collaborate with business, product, engineering and other teams to deliver practical, high-impact solutions.
• Take ownership of delivery, proactively managing risks, dependencies and changing priorities.
• Mentor junior data scientists through technical guidance, feedback and knowledge sharing.
• Evaluate new research, industry developments and methodologies, applying them where they add value.
• Use cloud-based machine learning services to develop scalable solutions.
Required Skills and Experience
Technical Skills
• Deep knowledge of statistics and mathematics, with experience designing rigorous analyses, testing assumptions and interpreting complex results.
• Strong data-wrangling and cleaning skills, including working with large or imperfect datasets, resolving quality issues and developing reproducible data pipelines.
• Strong machine learning experience, including model selection, feature engineering, validation, optimisation and performance monitoring.
• Proficiency in Python and relevant machine learning frameworks, with experience writing clear, tested and maintainable code.
• Ability to build domain knowledge quickly, assess relevant research critically and apply appropriate methods to business problems.
• Experience developing scalable data science solutions using GCP or comparable cloud platforms.
Ways of Working
• Takes ownership of project quality, delivery and business impact.
• Collaborates effectively across technical and business teams and communicates complex ideas clearly.
• Applies sound business judgement when prioritising work and balancing accuracy, value, cost and delivery time.
• Executes effectively in ambiguous environments and remains resilient when priorities change or experiments do not succeed.
• Maintains a continuous-learning mindset and shares knowledge across the team.
• Mentors junior data scientists and provides constructive technical guidance.
Experience and Qualifications
• Master’s degree or PhD in Data Science, Statistics, Mathematics, Computer Science or a related field, or a bachelor’s degree with significant equivalent experience.
• Significant experience delivering data science projects from problem definition through implementation and evaluation.
• Demonstrated proficiency in Python, machine learning frameworks and statistical analysis.
• Experience communicating recommendations to senior technical and non-technical stakeholders.
• Experience mentoring or technically guiding other data scientists.
Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
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