Job Purpose Statement

The Data Scientist is responsible for creating solutions that enable enhanced business solutions whilst mitigating / minimizing credit risks, through the use of advanced statistical analysis, data mining, and data visualization techniques.
The Data Scientist also plays a leading role in the management of:

• Build econometric and statistical models for various problems inclusive of projections, classification, clustering, pattern analysis, sampling and simulations
• Build the foundation of state-of-the-art scientific and technical capabilities within the Credit Quality Assurance department in order to support several planned and ongoing data analytics projects.
• Provide forward-thinking recommendations to the business by building in-depth understanding of the problem domain and available business data assets, especially those pertaining to strategic initiatives and value-based programs Execute ad-hoc data mining and exploratory statistics tasks on large data-sets related to the business 'strategies.
• Generate actionable insights applying advanced statistical techniques, for example, predictive statistical models, segmentation analysis, customer profiling, analysis, survey design, and data mining
• Collaborate with senior data scientists to communicate obstacles and findings to relevant stakeholders in an effort to improve decision-making and drive business performance.
• Review of the IFRS 9 model to provide assurance on accuracy of model inputs & outputs, as well as adherence to the end to end data loading, modeling and provisioning process

Ideal Job Specifications Academic:

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Machine Learning, Economics, or any other related quantitative field. Master’s degree or PhD in these fields is preferred. Professional: Big Data or Data Science certifications from recognized institutions

Desired work experience:

•  5+ years of professional experience in implementing full analytics pipeline (defining a business problem, data engineering, feature construction, modeling and implementation). Working with and maintaining open source data analysis platforms, including but not limited to: Scikit-Learn, TensorFlow, Spark, HDFS, Kafka and other high volume data tools. Expertise in SQL and NoSQL storage tools, such as MySQL, PostgreSQL, Cassandra, MongoDB and ElasticSearch.
• Previous Banking experience, particularly within the field of Credit Risk Management, with experience in IFRS 9 modelling and review