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A high impact digital financial services company, scaling rapidly across the continent is looking to appoint a world-class Lead Data Scientist. As they grow their customer base, enter new geographies, onboard new corporate channel partners, expand their business lines and roll out new products at a very rapid pace, their Lead Data Scientist needs to come from a strong analytical background that can take potentially ambiguous, real-world business problems and formulate statistical solutions, which can be implemented in a pragmatic way.
An understanding of the limitations and assumptions of such a modelling process with the ability to generate actionable insights and build effective predictive models, will be key in this role.
You’ll be expected to operate at the highest level on a constantly changing set of issues, understand the intricacies of a growing company in a regulated industry, hold your own with intelligent and opinionated staffers and do so while constantly learning new skills and topics. Success in this role means sidestepping the usual career ladder and getting a front-row seat to the art and craft of company building.
You will support their CEO and Executive Team to drive critical cross-functional initiatives structuring, organising data for just in time-critical decision making. This is a great opportunity for a detail-oriented professional with great influencing skills at a wider business level. The ideal candidate would have demonstrated strong stakeholder engagement skills when engaging with other business units as this position will work closely with Operations, Engineering, Customer Services, Risk Management, Finance and In-country teams across the whole business.
The ideal candidate will be proactive, flexible, practical, delivery-focused with excellent stakeholder management and communication skills. She/he will have a keen eye for detail, be passionate and resilient and will have a toolbox of tools and frameworks that have been tried and tested in previous workplaces to allow their business to gain trust and instil sustainability.
A laser focus on Delivery, Excellence and Outcomes!
- Broad and significant engagement in all steps of the problem-solving process, including specification, solution proposal, implementation and delivery
- Application of statistical modelling and machine learning techniques in credit decisioning and other critical business decisions
- Delivery of data driven solutions and predictions for a diverse range of business problems, including credit scoring, MIS, risk assessment, campaign management and contact prioritisation
- Development of real-time data pipelining and delivery of ML predictions. A/B testing and data analysis using statistical methods to generate insights and optimise existing and future business processes
- Using latest data mining, machine learning techniques solving supervised & unsupervised learning problems
- Creating workflows/frameworks for data engineering, model building and deployment as well as monitoring and improvements of these models
- Designing solutions for complex business problems related to Big Data by using NLP/ Machine Learning/ Text Mining techniques
- Developing & implementing solutions to fit business problems which may include applying algorithms from a standard statistical tool or custom algorithm development
- Analyze lending product portfolio on the credit risk perspective and look for profitable sales opportunities and future quality trends
- Support future risk management strategy and products offering with relevant analysis and insights
- Conduct projects related to data gathering and data pilots
- Document and suggest changes to policy rules and credit scoring strategy
- Work with large complex data sources such as financial transactions, non-financial, proxies, alternative data, credit bureau reports, marketing responses, KYC and CRM activity to extract meaning
- Design and manage the organisation’s MIS and “War Room” analytics
- Proactively provide data-driven insights and custom reports on critical areas of the business
- Responsible for monitoring and managing assumed Credit Losses
- Multi-disciplinary projects with the wider tech team and business stakeholders
- Lead a growing team of data scientists
Skills and experience;
- Strong analytical backgrounds in Physics, Mathematics, Engineering, Computer Science or related field etc; a PhD in any of these fields will be a plus
- Good understanding of the techniques used in statistical data analysis, ability to generate well founded insights from potentially unstructured data
- Some practical application of predictive modelling and pragmatic implementation of machine learning techniques to effectively solve real-world problems is a bonus
- Someone that isn’t satisfied with just applying out-of-the-box models, you want to know how they work, when they work and why they work
- Some practical object-oriented programming experience: Python, Java, C/C++ or similar
- Experience with some of the common Python data-science toolkits: Pandas, NumPy, SciPy, Scikit-learn is a bonus.
- Experience with R, Matlab, and/or SQL would be beneficial
- Self-starter with ability to work autonomously in a relatively unconstrained environment.
- A healthy scepticism and desire to build innovative, but practical, solutions to business problems
- Love for coding and getting hands-on with the raw data to really understand the underlying processes and nuances of the problem
- 5-10 years of cumulative post-university related professional experience with at least 3years in Credit and Lending business/operations
- Experience with working in multinational and multicultural environments
- Fast learner, avid reader on multiple topics with emphasis on start-ups, digital finance and business literature
- Strong problem-solving capabilities, both in ideation and execution
- Highly analytical and rigorous, demonstrably able to take a problem apart in real time
- Multitasking and time management skills
- Computer skills
- Organisational and administrative skills
- Attention to detail with analytical skills
- Working within a structured delivery environment and in accordance with best practices and standards
- Ability to work remote as and when needed, with own reliable internet connection