Data Science Analyst

Company Description

• This opportunity is advertised on behalf of a partner organisation. All applications, interviews, and subsequent hiring stages will be managed directly by the partner organisation.
• Our partner is seeking a Data Science Analyst to support the use of data across business, commercial, customer, and operational functions.
• The role combines data analysis, statistical thinking, business intelligence, and elements of Data Science to help teams understand performance, identify opportunities, and make evidence-based decisions.
• The successful candidate will work with business and technical stakeholders to investigate questions, interpret datasets, develop analytical solutions, and communicate findings in a clear and practical way.
• This is an opportunity for a data professional who enjoys moving beyond reporting to understand why patterns occur, what they mean for the business, and how data can inform future decisions. Key Responsibilities
• Explore and analyse datasets to answer business, customer, product, and operational questions
• Use Python, SQL, and statistical techniques to extract meaningful insights from data
• Perform data preparation, cleaning, validation, and quality checks across multiple data sources
• Conduct Exploratory Data Analysis (EDA) to investigate trends, relationships, outliers, and changes in performance
• Develop and maintain analytical reports, dashboards, KPIs, and performance metrics
• Monitor business and operational data to identify emerging trends and areas requiring further investigation
• Conduct statistical analysis and hypothesis testing to support evidence-based decision-making
• Design, analyse, and interpret experiments and A/B tests where appropriate
• Build data visualisations that make complex findings accessible to business and non-technical audiences
• Support forecasting, customer segmentation, trend analysis, and other analytical modelling activities
• Apply basic Machine Learning techniques where they provide value to a business or analytical problem
• Translate analytical findings into practical recommendations and clearly communicate their implications
• Work with stakeholders to define analytical requirements and establish appropriate metrics and success measures
• Identify opportunities to improve data processes, reporting efficiency, and analytical workflows
• Collaborate with Product, Commercial, Finance, Operations, Engineering, and other business teams
• Maintain clear documentation of analytical approaches, assumptions, methodologies, and findings Requirements
• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Business Analytics, or another quantitative discipline
• Strong working knowledge of Python and SQL
• Good understanding of statistics, probability, and quantitative analysis
• Familiarity with data preparation, Exploratory Data Analysis (EDA), and data quality principles
• Ability to work with datasets and identify meaningful trends, relationships, and anomalies
• Familiarity with pandas, NumPy, and other Python-based analytical libraries
• Understanding of fundamental Machine Learning concepts and their practical applications
• Familiarity with data visualisation and reporting tools such as Power BI, Tableau, Looker, or similar platforms
• Strong ability to communicate analytical findings clearly and concisely
• Strong written and verbal English communication skills
• Comfortable working with both technical teams and business stakeholders
• Strong attention to detail and a structured approach to problem-solving Preferred Qualifications
• Academic, internship, project, freelance, or professional exposure to Data Analytics, Data Science, Statistics, Business Intelligence, or a related field
• Experience working on projects involving customer, commercial, financial, product, or operational data
• Familiarity with experimentation, A/B testing, or statistical modelling
• Experience with Excel alongside Python and SQL
• Familiarity with Git and GitHub
• Experience working with Jupyter Notebook
• Exposure to cloud-based data environments such as AWS, Microsoft Azure, or Google Cloud Platform (GCP)
• Familiarity with data warehouses or modern analytics platforms such as BigQuery, Snowflake, Redshift, or Databricks
• Exposure to predictive analytics or Machine Learning projects
• Familiarity with Generative AI, Large Language Models (LLMs), or AI-enabled analytical tools
• Portfolio demonstrating practical analytical work through GitHub, Kaggle, academic projects, or personal projects