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