Decision Scientist (Customer Value Management) at Capitec Bank

About the Role

• This position within Capitec Connect requires a skilled individual with a keen analytical mindset, strong communication abilities, and a genuine enthusiasm for deriving insights from data.
• The incumbent of this role will collaborate extensively with cross-functional teams to scrutinize client data, construct predictive models, and offer actionable suggestions to bolster client retention, capitalize on cross-sell opportunities, and amplify overall client lifetime value.
• Responsibilities include analysing extensive datasets to pinpoint trends and patterns, crafting and sustaining dashboards and reports for tracking key performance metrics, partnering with stakeholders to comprehend business needs and devise analytical solutions, creating predictive models and segmentation strategies to identify valuable client segments and potential churn risks, conducting ad-hoc analyses to support strategic decision-making, and continuously evaluating the efficacy of client value management initiatives to propose optimization strategies grounded in data insights. It's essential to remain abreast of industry trends, best practices, and emerging technologies within the realms of data analytics and telecommunications to excel in this role.

Our Ideal Candidate

Qualification 

• Relevant B Degree, plus 5 years related experience. 

Minimum Experience

• Customer value management exposure
• Experience with data manipulation, exploration and visualization for example Power BI, SQL etc.
• Experience in statistical (predictive and classification) model development and deployment incl. traditional scoring (logistic regression with binning and missing value replacement e.g. reject inference and quantitative analysis (time value of money etc.)
• General business know-how: e.g. risk, compliance, operations e.g. NCR, POPIA, SARB
• Business analysis and requirements gathering.
• Functional business area (e.g. Telecommunications) environment knowledge and experience. 

Minimum Knowledge

• Understanding of state of the art statistical (predictive and classification) model development and deployment principles and techniques incl. traditional scoring (logistic regression with binning and missing value replacement e.g. reject inference).
• Underlying theory and application of machine learning models; able to understand underlying principles and theory.
• Best practices for decision science such as reusability, reproducibility, continuous monitoring, etc.

Minimum Skills

• Statistical model development incl. traditional scoring (logistic regression with binning and missing value replacement e.g. reject inference), and quantitative analysis (time value of money etc.)
• Data manipulation, exploration, and visualization for example Power BI, SQL etc.
• Capability to identify, define and solve the business problem i.e. application of domain 'know-how' and problem solving. 
• Ability to productionise and solution design