Lead Risk Analyst

3 weeks ago

Cape Town, South Africa Progressive Edge Full time

**Lead Risk Analyst**
**Permanent**
**Cape Town**
**Purpose of the Job**

The purpose of the Lead Risk Analyst role is to leverage companys vast operational data to execute analysis with predictive modelling in order to shape tactical and strategic initiatives that drive risk mitigation, loss prevention, general assurance and overall customer centric experiences in-store. This includes optimising and integrating data with various in-store processes and financial models to define and enhance practices or identify opportunities. The role builds insights and/or tools that identifies risks across the value chain in all parts of the business, providing concrete inputs that guide the team to either enhance, slow down or stop activities based on the trajectory of data findings and forecasts. The role leads a specialised data science and analytics team and interacts with various cross-functional teams with a strong collaborative approach to achieving predictive models that provide assurance between the Finance, Loss Prevention and Audit functions.
**Job Objectives**
- Identifying clear and accurate data points that enable proactive data analytics that supports operational analysis, strategic analysis, and planning inputs.
- Identifying primary in-store retail operations drivers and the appropriate analytical approach to categorize, define, dissect and interpret data (e.g., fraud detection and prevention).
- Working with the local teams to gather input and execute urgent data insights to drive risk-mitigating / loss prevention and compliance decision making and supporting successful operational execution and performance.
- Monitoring all planning and execution activities of the risk analytics team providing performance reports, templates, and presentations that communicate the analysis undertaken planning activities derived from the analysis, costing models applied, and outcome of execution.
- Working cross-functionally and actively participating in open and honest team discussions. Maintaining alignment across the team during data extraction, analysis, planning, and execution.
- Spending time in stores to appreciate opportunities using data, the nature of process flows, and first-hand account of whats working and could potentially be improved.
- Participating in defining business problems by asking appropriate questions and proposing a specific analysis approach (full data pipeline).
Working with the business to identify and prepare data for data mining or creating statistical models.
- Ensuring the stability of the existing systems environment by implementing solutions and data models that do not compromise operational stability.
- Participating in post-implementation reviews using data to inform the discussion.
- Drive continuous improvement within the team and workshop solutions to existing processes and challenges. Adhering to internal documentation, processes, protocols, and standards.
- Monitoring and assessing global best practices in risk analytics. Building an understanding of the companys data landscape, particularly as it pertains to in-store retail operational data and financial data.
- Leading a team of data scientists delivering a proactive and professional service to businesses that supports risk mitigation, loss prevention, compliance, and assurance.
**Requirements**:

- Degree or diploma in Finance, Commerce, Computer Science or equivalent

Essential
- 6+ years experience in a financial, or business context facilitating data analytics to guide the business process or decision making.
- Extensive practical experience across the retail operations value chain, with demonstrated understanding of in-store retail operations and financial risk.

Desirable
- Exposure to leading a small team of specialists to mitigate risks and build operational efficiencies.
- Knowledge of legislation relevant to the collection, storage, and exploitation of customer data.
- Knowledge of data structures relevant to both operational and business intelligence systems.
- Strong report writing skills in Tableau software or similar.
- Strong understanding of data mining models, structures, theories, principles, and practices.
- Knowledge of Business Intelligence and Data Warehousing.
- Knowledge of AWS / cloud. Skilled in using the open-source statistical package R, Python, or similar.
- killed in using SQL in the data mining process, particularly for data preparation.

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