Data Scientist

Company Description

Standard Bank Group is a leading financial services group focused on Africa, and an innovative contributor on the international level, offering a range of opportunities for career advancement – along with the chance to work with some of the industry’s most talented and driven professionals. Our clients are diverse, ranging from individuals, businesses of all sizes, affluent families to large multinational corporations and institutions. We are deeply passionate about fostering growth in Africa. We aim to deliver true, meaningful value to our clients and the communities we serve, instilling a genuine sense of purpose in what you do.

Job Description

Implement data mining techniques and carry out statistical analysis on large, structured and unstructured data sets to comprehend and analyze events. Model intricate business problems, revealing insights and opportunities through statistical, algorithmic, machine learning and visualization techniques, working closely with clients, data and technology teams to convert data into critical information for making sound business decisions. Execute intelligent automation and predictive modeling.

• Technology & Architecture: Develops machine learning models from and uses distributed data processing and analysis methodologies. Proficient in Machine Learning programming in R or Python, with supplementary skills in Matlab, Java, etc. Acquainted with the Hadoop distributed computation platform, including broader ecosystem of tools such as HDFS / Spark / Kafka.
• Risk, Regulatory, Prudential & Compliance: Contributes to Data management and modeling infrastructure requirements and adheres to the organization's infrastructure development processes, including the management of User Acceptance Testing (UAT). Conducts regression testing across all relevant systems as required.
• Client: Facilitates business integration by incorporating model outcomes into endpoint production systems, where requirements related to data collection, integration and retention incorporating business requirements and knowledge of best practices must be understood and followed.

Qualifications

• Undergraduate Degree (Information Studies/Information Technology) - Minimum
• Post Graduate Degree (Information Studies/Information Technology) - Preferred

Other Minimum Qualifications, Certifications

• Competence in application and web development. Structured and Unstructured Query languages e.g. SQL, Qlikview; Tableau; SSIS SSRS, Python JSON, C#, Java, C++, HTML

Additional Information

Experience Required

5 - 7 years: Demonstrated development experience in software and software engineering. Understanding of financial services data processes, systems, and products. Experience in technical business intelligence. Knowledge of IT infrastructure and data principles. Project management experience. Familiarity with governance and regulatory matters related to data. Experience in constructing models (credit scoring, propensity models, churn, etc.)

5 - 7 years: Experience in handling unstructured data (e.g. Streams, images). Understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Application of data mining to identify new patterns from large data sets. Standard and proprietary algorithm implementation for data handling and processing. Experience with common data science toolkits such as SAS, R, SPSS, etc. Experience with data visualization tools, such as Power BI, Tableau, etc.

Behavioral & Technical Competencies

Adopting Practical Approaches: Emphasizes learning by doing and implementing practical solutions with an emphasis on common sense when needed. Key to ensuring the organization implements viable solutions.

Articulating Information: This competency is about effectively expressing ideas and concerns, giving presentations to others and showcasing confidence in interacting with unfamiliar and familiar people alike.

Challenging Ideas: This competency encourages individuals to facilitate or catalyze change in an organization. It requires attitudes associated with questioning assumptions, challenging established views and arguing personal perspectives.

Data Analysis: Ability to analyze statistics and other data, interpret and evaluate results, and create reports and presentations for others' use.

Data Integrity: The ability to ensure the accuracy and consistency of data for the duration that the data is stored, as well as preventing unintentional changes or loss of data.

Database Administration: Refers to the knowledge and experience necessary to handle the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases.

Diagramming & Modeling: Measures proficiency in employing the diagramming and modeling techniques vital for requirements analyses.