Senior Data Scientist

• 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.

• 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.

• 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.

• 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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