Data Scientist

The Data Scientist is responsible for leveraging advanced analytics machine learning and statistical modelling to extract actionable insights from complex datasets. This role supports strategic decision-making drives innovation and enhances operational efficiency across the organisation.

Key Responsibilities

Advanced Data Analysis & Modelling

Develop implement and maintain predictive and prescriptive models using machine learning algorithms to forecast business outcomes enabling proactive decision-making and strategic planning.

Analyse large and complex datasets using statistical techniques to uncover patterns and trends driving data-informed insights and operational improvements.

Monitor model performance using validation metrics and retrain models as needed to maintain accuracy ensuring continued relevance and reliability of outputs.

Translate business challenges into analytical problems using structured frameworks enabling the development of targeted and effective data solutions.

Data Engineering & Management

Collaborate with data engineers to build robust data pipelines and ensure data integrity.

Maintain and optimize data storage solutions for scalability and performance.

Identify opportunities for automation in reporting and analysis using scripting and APIs increasing efficiency and reducing turnaround time.

Document methodologies assumptions and outcomes in a clear and reproducible format to support transparency governance and knowledge sharing.

Business Intelligence & Strategic Insights

Translate complex data into actionable insights that support strategic decision-making.

Identify trends patterns and anomalies that inform business strategies and operational improvements.

Develop and maintain dashboards and reports for various business units.

Solution Development & Deployment

Build end-to-end data science solutions from prototype to production.

Integrate models into business applications or platforms using APIs or other deployment methods.

Monitor deployed models for performance drift and retrain as necessary

Stakeholder Engagement & Communication

Work closely with business stakeholders to understand requirements and define analytical approaches.

Communicate findings clearly through presentations visualisations and storytelling to enhance stakeholder understanding and engagement.

Provide training and support to non-technical users on data tools and insights to build analytical capacity empowering teams to leverage data independently.

Innovation & Continuous Improvement

Experiment with new techniques to improve model performance and analytical capabilities fostering innovation and continuous improvement.

Contribute to the development of best practices standards and frameworks within the data science team to ensure consistency and quality.

Governance Compliance & Ethical Use of Data

Ensure compliance with data privacy regulations by applying ethical data managing practices protecting sensitive information and maintaining stakeholder trust.

Implement model governance practices including documentation versioning and audit trails.

Apply bias mitigation techniques in model development to ensure fairness accuracy and responsible AI practices

Role Competencies

Deep understanding of statistical methods probability theory linear algebra and calculus to support model development and data interpretation.

Advanced proficiency in Python R SQL and familiarity with Java or Scala. Ability to write clean efficient and reusable code.

Experience with supervised and unsupervised learning deep learning frameworks (e.g. TensorFlow PyTorch) and model evaluation techniques.

Knowledge of data warehousing ETL processes and working with structured and unstructured data.

Familiar with cloud platforms (AWS Azure GCP) containerization (Docker Kubernetes) and scalable data solutions.

Skilled in using tools like Power BI Tableau

Analytical & Problem-Solving Skills

Ability to approach problems logically identify root causes and propose data-driven solutions.

Understands business operations and can align data science initiatives with strategic goals.

Continuously seeks new methods tools and approaches to improve analytical outcomes and business impact.

Communication & Influence

Capable of translating complex data findings into clear compelling narratives for diverse audiences. Builds strong relationships with internal and external stakeholders understands their needs and delivers relevant insights.

Confident in presenting technical content to non-technical audiences including executives and decision-makers.

Collaboration & Teamwork

Works effectively with product managers engineers analysts and business leaders to co-create solutions.

Comfortable working in iterative environments adapting to changing priorities and feedback.

Adaptability and Agility

Demonstrates the ability to navigate ambiguity with confidence and composure.

Adapts effectively to shifting priorities evolving goals and dynamic business contexts.

Contributes proactively to refining processes structures and ways of working to support organisational growth.

Brings strong problem-solving skills flexibility and resilience coupled with a learning and growth mindset to thrive in an agile high-growth environment.

Requirements

• Bachelors degree in data science Computer Science Statistics Mathematics or a related field.
  
• 35 years of experience in data science analytics or a related field.
  
• Proven experience with machine learning predictive modelling and statistical analysis.
  
• Strong proficiency in Python R SQL and data visualisation tools (e.g. Power BI Tableau).
  
• Experience with cloud platforms (e.g. AWS Azure GCP) and big data technologies (e.g. Spark Hadoop) is advantageous.
  
• Familiar with version control systems (e.g. Git) and collaborative development practices.
  
• Advantageous
  
• Masters degree in data science Computer Science Statistics Mathematics or related field.
  
• Experience in healthcare retail or insurance data ecosystems