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Data Science Analyst
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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
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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
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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
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