Data Scientist

Overview

The Role

Purpose:

We are seeking a Data Scientist to join our team, with a primary focus on analysing complex datasets, developing predictive and statistical models, and generating actionable insights that enable smarter business decisions.

The successful candidate will combine strong analytical and problem-solving capabilities with practical experience in machine learning, statistical modelling, and data-driven decision-making. This role focuses on leveraging data to solve business challenges through forecasting, predictive analytics, and advanced modelling techniques.

The successful candidate will play a key role in helping the organisation unlock value from its data and build scalable analytical solutions that drive business outcomes.

• Analyse large and complex datasets to identify trends, patterns, and opportunities for business improvement.
• Develop, test, and deploy predictive and statistical models to solve business problems.
• Build and maintain data models that support business intelligence and decision-making initiatives.
• Design and implement machine learning solutions for use cases such as customer churn prediction, fraud detection, forecasting, and customer segmentation.
• Prepare, clean, and transform structured and unstructured data for analysis and modelling.
• Conduct exploratory data analysis and communicate findings and recommendations to stakeholders.
• Develop and maintain reporting, dashboards, and analytical solutions that provide actionable insights.
• Monitor, evaluate, and improve the performance and accuracy of predictive models.
• Collaborate with business stakeholders to understand requirements and translate them into data-driven solutions.
• Work closely with Data Engineers and technology teams to ensure data quality, accessibility, and governance.
• Document methodologies, assumptions, and analytical processes to ensure repeatability and knowledge sharing.
• Stay informed of emerging technologies and best practices within Data Science and analytics.

Note: The following sections outline the ideal candidate’s educational background, work experience, knowledge, and technical skills. (Text kept as originally provided.)

• Educational Background: Bachelor's Degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Technology, or a related quantitative field.
• Postgraduate qualification in Data Science, Applied Mathematics, Statistics, Machine Learning, or Artificial Intelligence is advantageous.
• Relevant certifications in Data Science, Machine Learning, or cloud platforms are advantageous.

• Work Experience: 3–5 years of experience as a Data Scientist delivering analytical and predictive solutions in production environments.
• Proven experience developing and deploying machine learning and statistical models to solve business problems.
• Experience working with large datasets and building data-driven solutions that deliver measurable business value.
• Experience collaborating with cross-functional teams and translating business requirements into analytical solutions.
• Exposure to cloud-based data platforms and modern data ecosystems is advantageous.

• Knowledge: Strong understanding of machine learning algorithms, statistical modelling techniques, and predictive analytics.
• Knowledge of data preparation, feature engineering, and model evaluation methodologies.
• Understanding of data modelling principles and analytical frameworks.
• Familiarity with data governance, data quality, and best practices for handling enterprise data.
• Exposure to Generative AI technologies and Retrieval-Augmented Generation (RAG) concepts is advantageous but not required.

• Technical
  
  Skills:
  
  Data Science & Analytics:
  Python for data analysis and machine learning; SQL and relational databases;
  Statistical modelling and predictive analytics;
  Data wrangling, cleansing, and feature engineering;
  Data visualisation and reporting.

• Machine Learning: Supervised and unsupervised learning techniques;
  Model evaluation and performance optimisation;
  Forecasting and predictive modelling;
  Classification and regression techniques.

• Data Platforms & Tools: Experience with cloud data platforms such as Azure, AWS, or GCP is advantageous;
  Experience with data warehouses such as Snowflake, Big Query, or similar platforms is advantageous;
  Familiarity with notebooks and analytical tools such as Jupyter.

• AI & Emerging Technologies (Advantageous): Exposure to Large Language Models (LLMs) and Generative AI concepts;
  Familiarity with Retrieval-Augmented Generation (RAG) principles;
  Exposure to frameworks such as Lang Chain is advantageous but not required.

• Engineering & Delivery: Strong problem-solving and analytical thinking capabilities;
  Ability to communicate technical concepts and insights to both technical and non-technical stakeholders;
  Experience working in Agile delivery environments;
  Strong documentation and presentation skills.