Manager, Data Science (Machine Learning)

Manager, Data Science (Machine Learning) Job Overview Business Segment: Archived_01_Corporate Functions Location: ZA, GP, Johannesburg, Baker Street 30 To assist with advanced analytics and deep insight by being a proactive partner in providing customer centric data analytics, including alternative methods of aggregating raw data (internal and external), which will ultimately influence the way in which we view and act on customer behaviour and customer health, i.e. identifying risks and opportunities. Implementing the use of machine learning to challenge and improve predictive modelling techniques, the available characteristic universe across the customer life cycle and optimising segmentation to enhance model performance. Solutions should satisfy customer centricity and digitisation objectives. Including but not limited toExtracting meaningful insights from data,Predictive modelling and machine learning,Stakeholder Engagement, Leadership and People Management. Qualifications Minimum QualificationsType of Qualification: Post Graduate DegreeField of Study:Mathematical SciencesOther Minimum Qualifications, Certifications or Professional Memberships: Honours degree (with majors in Statistics / Applied Mathematics / Econometrics / Actuarial Sciences/Engineering.Experience Required5-7 Years experience in advanced analytics, combined with sufficient knowledge of products and customer behaviour in a financial services environment.Some experience in managing a quantitative team at a junior management level. Experience with data mining and retail credit risk modelling.Communication skills, in particular, communication of technical concepts to a non-technical audience.Team management and leadership experience as well as interaction at executive level. Coding ability and experience to deal with big datasets (preferably in SAS, R, Python or SQL).Some experience of machine learning modelling techniques, with an understanding of underlying details and parameters. Good analytical problem-solving skills.Experience in developing machine learning or AI models.Experience with container technologies (e.g. Docker, Kubernetes)Experience with cloud-based infrastructures Azure and/or AWS with their MLOps tools.

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