About the position
The Senior Data Scientist will lead the design, development, and deployment of advanced analytics and machine learning solutions that drive strategic decision-making and operational efficiency. This role requires a deep understanding of data science, data engineering, and AI concepts, and will play a pivotal role in embedding intelligent automation and predictive modelling across the organisation.
Duties and responsibilities:
- 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.
Role Competencies:
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:
Effectively communication of complex analytical insights through compelling visualisations and narratives
RequirementsQualification & Experience:
- 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.
Advantageous:
- 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
BenefitsThis role follows a hybrid work model, allowing flexibility in where you work while requiring in-person presence when operational needs arise.
Desired Skills:
- Python
- R
- SQL
- ML
- Cloud Data Architectures
- MLOps tools
Desired Qualification Level:
About The Employer:
