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

Work arrangement
Yes