Senior Data Scientist Cvm

Job Description- To undertake predictive modelling and the design of data products within ABF Credit Analytics which either drive innovation or add value by a) reducing risk b) increasing sales/cross-selling/revenue/profits c) improve efficiency d) retaining existing customers or e) a combination of the above. Examples include clustering/hyper segmentation, propensity modelling, attrition modelling, data-driven insights that include channel behavior, preferred payment mechanism, attrition unpacking, ideal customer behavior, etc.
- To support the sales teams by means of deriving of value/ insights from data which can be used to enhance and inform strategic decisions and ultimately add more value to the business as a whole.
- Be heavily involved in the design of solutions for problems faced by stakeholders, i.e. performing initial investigations into the problem and proposing a solution, building a business case for potential solutions, be heavily involved in POCs including A/B testing, etc. Also work closely with stakeholders to ensure the correct solution is designed and implemented.
- Assist with ad-hoc queries for stakeholders regarding existing solutions, newly implemented solutions or completely new solutions.
- Drive implementation of solutions on production environments.
- Provide analytical support with regards to the implementation of CVM X/ Offer X/ Event X use cases.
- Build/ guide other team members to build PowerBI reports that display valuable insights and trends.
- Participate in code and peer reviews.
- Support the Credit Acquisitions team from time to time
- Innovate by finding the best fit solution for the situation such as the flexibility of delivery and customized solutions which result in more efficient outcomes.
- Provide input into localisation of appropriate model building policies, standards, frameworks, and governance process and integration of business modelling and analytics value chain.
- Adhere to model building policies, standards, frameworks, and governance process.
- Ensure own ethical usage of information that complies to restrictions applied for privacy and sensitivity classification.
- Understand and ensure implementation and adherence to Privacy business requirements, legislation and policy.
- Ensure reporting of any Information Privacy Incidents and escalate appropriately for resolution of Privacy and Protection Audit Findings, Complaints, Incidents, Investigations and Privacy and Protection Assessments.
- Create delivery plans for achievement of outputs / deliverables in collaboration with business stakeholders to understand information requirements for strategy delivery and define metrics that track performance.
- Analyse available data to identify information commercialisation opportunities that have not been explored to address business needs and revenue options.
- Productionize analytics in the approved FirstRand architecture by translating technical data information into practical business information that addresses identified needs.
- Analyse information requirements, availability and quality of data to feed into management for resolution.
- Contribute to the development of core analytical capabilities or model libraries using advanced statistical, quantitative or econometric techniques and utilise as appropriate.
- Collaborate with numerous departments across the business to aid them in the proper use of data to ensure delivery of desired operational results.
- Participate in relevant project related to the business overall analytical needs and opportunities.
- Build models that analyse processes to recommend areas for optimisation to achieve cost savings, revenue generation or efficiency improvements for the business.
- Document and audit relevant processes.
- Document and implement models to address specific business requirements.
- Conduct appropriate manipulation and analysis of data to pro-actively identify and meet needs of the business for the purpose of future work and to ensure high information-quality and reliability across the business.
- Implement Machine learning mathematical algorithms and automation techniques using open-source programming languages such as SAS or Python libraries.
- Utilise advanced Predictive modelling techniques to hold insight into outcomes and future events that confront key assumptions.
- Utilise data to model complex abstractions in machine learning research to enable deep learning.
- Utilise pattern recognition technology to recognizes patterns in data and the criteria for use interchangeably with machine learning technology.
- Conduct Text analytics to examine unstructured data to glean key business insights.
- Understand principles of Data Wrangling to identify and recommend corrective actions for imperfections in data quality.
- Participate in enterprise-wide high level problem resolution by interacting with relevant engineers and product managers, utilising appropriate methods and decide when approximations make sense.