Roles and Responsibilities:
- Develop best-in-class predictive statistical models and algorithms
- Conducts advanced statistical analysis
- Provide actionable insights, identify trends, and measure performance
- Create value out of enterprise-wide data
- Provide insights to BI and BA to further exploit and present their models
- Provide strategic direction ito data information received
- Apply advanced analytical techniques such as machine learning and artificial intelligence in order to derive business value.
- Conduct data discovery for inclusion in models.
- Collaborate with key stakeholders to obtain business acumen and intellectual property
- Prevent wastage and identify process improvements to contain and reduce costs
- Utilise, refine, and enhance advanced statistical models and data analysis to inform decision making and address business needs
- Contribute to creative business solutions, optimisation of processes and conduct statistical modelling and data analysis to inform strategic decisions, under limited supervision
- Assist in the delivery of value-add outputs across the analytics value chain in delivery of business strategy
- Implement localised Analytics strategy to address business needs, under limited supervision
- Develop new insights into situations and apply innovative solutions to make organisational improvements
- Build working relationships across teams and functional lines to enhance work delivery collaboration and innovation
- Keep abreast with latest tools and techniques
- Understands business problems and designs end-to-end analytics use cases
- Collaborates with model developers to implement and deploy scalable solutions
- Develop complex models and algorithms that drive innovation throughout the organization.
- Provide thought leadership by researching best practices, conducting experiments, and collaborating with industry leaders.
- Work with large data sets, simulation/ optimization and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, Gurobi, Arena, etc.).
- Ensure understanding of pros and cons of different analytics approaches for problem type.
- Implements more advanced algorithms, e.g., complex descriptive
- Coding ?? SAS, SQL,Python, VBA
- Familiarity with visualization tools (e .g. Tableau, PowerBI, Qlik, D3).
- Identify data risks and mitigate these (pre, during & post solution deployment / data delivery)
- Create business cases & solution specifications for various governance processes (if required)
- Apply data quality assurance frameworks and tools to guarantee data quality & data integrity (always) for specific data solutions
- Contribute to risk, governance, compliance & broader regulatory processes as a data science expert (if & when required)