Intermediate Data Scientist Hybrid

Our client is an established global logistics company that has been operating since 2001. They have a large global market share, but an ambition to continue to expand. They have five internal products, one of which is the only one of its kind in the logistics and freight industry. This role will be focused on internal and external projects, with a forthcoming focus being a migration to cloud.

• Partner with business stakeholders to understand analytical requirements and support data-driven decision making.
• Develop statistical models and analytical solutions to solve complex business problems.
• Design, execute and evaluate analytical experiments to generate actionable insights.
• Analyse large and complex datasets to identify trends, patterns and opportunities.
• Build, test and refine predictive models and algorithms, performing data validation and error analysis to improve accuracy and performance.
• Work closely with software engineering teams to deploy analytical models into production environments.
• Create engaging reports, dashboards and visualisations using PowerBI.
• Stay up to date with emerging technologies, industry trends and best practices within Data Science and Analytics.
• Commercial experience working in a Data Science, Analytics or Machine Learning focused role.
• Strong understanding of statistical concepts and techniques, including regression analysis, hypothesis testing, probability distributions and model evaluation.
• Experience using Python for data analysis and model development.
• Strong problem-solving abilities with the confidence to tackle complex business challenges.
• Ability to translate business requirements into practical analytical solutions.
• Excellent communication skills, with technical and non-technical stakeholders.
• A collaborative approach and the ability to work effectively within cross-functional teams.
• Strong attention to detail and a proactive, inquisitive mindset.
• Experience with R for statistical analysis.
• Exposure to cloud platforms such as Microsoft Azure or AWS.
• Understanding of software development principles, architecture and deployment processes.
• Knowledge of Machine Learning model deployment and MLOps practices.