Senior Data Scientist

SUMMARY:
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POSITION INFO:

The Data Scientist will work within the Data Science and Analytics team to deliver data-driven insights, predictive models, and automated solutions that address complex business challenges. The role requires strong expertise across the full data science lifecycle, including business understanding, data exploration, feature engineering, model development, validation, and deployment into production environments. By leveraging advanced analytics and machine learning, the Data Scientist will contribute to strategic decision-making, process optimisation, and innovation across the organisation.

Key Responsibilities

• Research and test innovative data science techniques that can be utilised in predictive and prescriptive analytics solutions.
• Stay informed on emerging data science trends, technologies, and applications relevant to industry advancements.
• Collaborate with business stakeholders to identify problems/opportunities, elicit requirements, and define expected outcomes.
• Partner with stakeholders to design and propose data-driven approaches that address business needs and support new strategies.
• Develop conceptual models and solutions to meet business requirements.
• Work with subject matter experts and data engineers to identify, obtain, and prepare relevant data sources.
• Perform preprocessing of structured and unstructured data, including manipulation, transformation, normalisation, standardisation, visualisation, and feature engineering.
• Apply appropriate data mining, statistical, and machine learning techniques to solve business problems.
• Implement predictive and prescriptive models on large-scale datasets, including distributed computing platforms (e.g. Spark).
• Validate and test models using methods such as back-testing, A/B testing, and scenario modelling.
• Monitor, refine, and maintain models to ensure continued accuracy, relevance, and business value.
• Profile, visualise, and interpret data to inform modelling approaches and provide actionable insights.
• Review existing models and code, ensuring quality and identifying improvements.
• Generate reports, dashboards, and visualisations that effectively communicate insights to technical and non-technical audiences.
• Ensure compliance with applicable policies, procedures, regulations, and professional standards.
• Contribute to the ongoing review and enhancement of internal data science policies and practices.

Experience Required

• Minimum of 5–6 years’ experience in data science projects, including the delivery of predictive and prescriptive models.
• Proven proficiency with Python, including machine learning libraries and frameworks (NumPy, Pandas, SciPy stack, Matplotlib, scikit-learn), and experience working in Jupyter notebooks.
• Strong SQL skills and experience handling large, complex datasets.
• Demonstrated application of machine learning and statistical techniques to real-world business problems.
• Experience working in agile development teams.
• Proven ability to operationalise and productionise data science solutions.
• Exposure to high-scale production environments.

Knowledge and Skills Required

• Strong understanding of the Data Science Development Cycle, including problem framing, data profiling, feature engineering, model building, evaluation, and productionisation.
• Outstanding problem-solving and analytical abilities, with the ability to conceptualise and test hypotheses.
• Ability to clean, unify, and integrate structured and unstructured datasets.
• Proficiency in data visualisation and communication tools (e.g. Tableau, Power BI, Kibana).
• Solid programming experience (Python / Java) based on prepared designs.
• Familiarity with modern big data platforms and distributed processing technologies (e.g. Hadoop ecosystem, Spark, Kafka, HDFS).
• Strong understanding of ETL processes, data flows, and big data architectures.
• Experience designing comprehensive solutions aligned with business and technical requirements.
• Ability to effectively communicate insights, trends, and correlations to diverse audiences with varying technical expertise.
• Strong report writing skills with clear visualisations and concise commentary.

Inherent Requirements

• Degree (Honours, Masters, or PhD) in a quantitative field such as Statistics, Mathematics, Computer Science, Actuarial Science, or Engineering.
• Professional certifications in Data Science or related technologies (e.g. Python, Azure, AWS, Spark, Machine Learning, Big Data, Cloud Infrastructure).
• Minimum 5 years’ hands-on experience in data science and analytics initiatives.
• Proven ability to apply machine learning techniques and deploy solutions into production.

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