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

• Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, or a related field.
  
• 3–5 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
  
• Master’s degree in data science, Computer Science, Statistics, Mathematics, or related field.
  
• Experience in healthcare, retail, or insurance data ecosystems