•  Build and implement machine learning models using structured and unstructured data to improve forecasting accuracy and enable proactive decision-making.
  
• Optimise model performance and scalability through hyperparameter tuning and algorithm selection to enhance efficiency and reduce computational costs.
  
• Implement reproducible research practices by using version control, documentation, and testing to maintain model integrity and facilitate collaboration.
  
•   Monitor deployed models in production using performance metrics and alerting systems to ensure reliability and timely intervention.
  
• Automate repetitive data science tasks through scripting and workflow orchestration to increase productivity and reduce manual errors.
  
• Maintain high data quality standards by conducting regular audits and validation checks to support trustworthy analytics.
  
•  Translate complex analytical findings into clear, actionable insights for non-technical stakeholders to drive informed business strategies.
  
•  Present data-driven recommendations using compelling visualisations and storytelling techniques to influence executive decision-making.
  
• Collaborate with stakeholders to define key metrics and success criteria to align analytics efforts with business goals.
  
• Collaborate with data engineers to streamline data ingestion and transformation processes using scalable architectures to reduce latency and improve model performance.
  
• Identify and implement novel AI use cases through research and experimentation to enhance business capabilities and competitive advantage.
  
• Communicate complex analytical findings through visualisations and storytelling to influence strategic decisions and operational improvements.
  
• Mentor junior data scientists and analysts through code reviews, knowledge sharing, and career guidance to build team capability and foster growth.
  
•  Contribute to the development of best practices, standards, and frameworks within the data science team.
  
• Handles ambiguity and setbacks constructively, maintaining focus on long-term goals.
  
• Implement responsible AI practices and adhere to data governance policies to maintain trust and regulatory compliance.
  

Machine Learning

·        Expert in designing, developing, and deploying advanced machine learning and AI models.

·        Expert in selecting appropriate algorithms, optimising model performance, and mentoring junior team members in best practices.

Data Engineering & Architecture

·        Understanding of ETL/ELT processes and data pipeline design.

·        Ability to collaborate with data engineers to ensure data quality and accessibility.

Programming & Tooling

·        Advanced proficiency in Python, R and SQL

·        Use of Jupyter, VS Code, Git, and other development tools.

·        Contribute to code reviews and promotes clean, maintainable code practices

Cloud-Native ML Tools & Platforms

·        Proficiency in deploying models using platforms like AWS SageMaker, Azure ML, or Google Cloud AI Platform.

·        Familiarity with containerisation (Docker) and orchestration (Kubernetes) for scalable ML solutions.

Data Visualisation and Storytelling:

Requirements

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
  
•  6–8+ years of experience in data science, with at least 2–3 years in a senior or lead role.
  
• Proven experience in developing and deploying machine learning models in production environments.
  
• Strong proficiency in Python, R, SQL, and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
  
• Solid understanding of data engineering principles and cloud data architectures (e.g., Azure, AWS, GCP).
  
• Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow).
  
• Excellent communication and stakeholder engagement skills.
  

• Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
  
•  Experience with large language models (LLMs) and generative AI.
  
• Experience in healthcare, retail, or insurance data ecosystems
  

Benefits

This role follows a hybrid work model, allowing flexibility in where you work while requiring in-person presence when operational needs arise.