Data Scientist

Senior Data Scientist

Key Responsibilities:

• Apply advanced statistical and machine learning techniques to solve complex business challenges and identify opportunities for growth and optimisation.
• Design, develop, validate, and deploy predictive models and advanced analytical solutions, ensuring they are robust, scalable, and fit for purpose.
• Perform exploratory data analysis, hypothesis testing, customer segmentation, forecasting, and experimentation to generate actionable insights.
• Translate complex analytical findings into clear, data-driven recommendations that support business and strategic decision-making.
• Partner closely with business stakeholders to understand objectives, define analytical approaches, and ensure solutions address real business needs.
• Communicate analytical results through compelling data storytelling, clear visualisations, and executive-level presentations.
• Collaborate with Data Engineering, Product, Technology, Marketing, and Business teams to operationalise models and embed data-driven insights into business processes.
• Ensure analytical solutions are explainable, reliable, and aligned with business objectives and expected outcomes.
• Contribute to the continuous improvement of data science methodologies, tools, processes, and best practices across the organisation.
• Stay up to date with emerging developments in data science, machine learning, MLOps, and Generative AI, identifying opportunities to apply new technologies to business use cases.

Education & Experience:

• BSc or MSc in Computer Science, Data Science, Data Engineering, Statistics, Mathematics, or a related quantitative field.
• 4–6+ years of hands-on experience in Data Science, Data Analytics, Software Engineering, or Data Engineering, with strong exposure to analytical and predictive modelling.
• Strong understanding of statistics, experimental design, forecasting, predictive modelling, and machine learning.
• Practical experience with machine learning techniques such as regression, decision trees, random forests, gradient boosting, clustering, and classification.
• Strong programming skills in Python and/or R, combined with solid SQL capabilities.
• Experience working with data visualisation and business intelligence tools such as Power BI and/or Tableau.

Business & Stakeholder Skills:

• Strong ability to translate complex analytical concepts and findings into clear, actionable business recommendations.
• Excellent communication, presentation, and data storytelling skills, with the ability to engage both technical and non-technical audiences.
• Demonstrated ability to influence decision-making through data-driven insights.
• Experience in customer, marketing, commercial, or digital analytics is highly valued.
• Strong stakeholder management skills and the ability to build effective relationships across different functions and seniority levels.
• Experience working in cross-functional and matrix organisations.

Nice to have:

• Exposure to MLOps practices and the deployment and monitoring of machine learning models.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Experience with Databricks or similar data and AI platforms.
• Exposure to Generative AI and its applications in business and analytics.
• Experience working with modern data and analytics ecosystems at scale.

Key Technologies

Python and/or R | SQL | Statistical Modelling | Predictive Modelling | Machine Learning | Power BI and/or Tableau

Nice-to-have Technologies

MLOps | Azure | AWS | GCP | Databricks | Generative AI