Senior Data Scientist

Senior Data Scientist

Minimum Requirements:

• Matric (Grade 12)
• Advanced Diplomas/National 1st Degrees

Responsibilities:

• Spearheaded best-in-class statistical models and algorithms, building upon previous experiences and learnings.
• Conduct in-depth statistical analysis to extract valuable insights and patterns from complex datasets, contributing to data-driven decision making.
• Offer actionable insights and advice to stakeholders, utilizing a solid foundation in AI/ML and contributing to the team's expertise.
• Contribute to the creation of value from enterprise-wide data, assisting in the translation of data into meaningful business solutions.
• Apply specific financial services domain knowledge to analyse datasets and develop statistical models and algorithms that cater to individual financial services use cases.
• Design and implement ML models with experienced banking professionals that meet the unique requirements of financial institutions.
• Implementation of cutting-edge AI and ML solutions, playing an active role in system operations and maintenance.
• Experienced in deploying or contributed to deployment of at least one end to end data science solutions that has yielded significant value in the organisation at an enterprise level.
• Contribute to the shaping of the organization's AI/ML strategy, aligning it with evolving business needs.
• Assist in transforming data science prototypes into scalable machine learning solutions for deployment.
• Collaborate with experienced team members to design dynamic ML models and systems, incorporating the capability for adaptability and retraining.
• Participate in periodic evaluations of ML systems, ensuring they align
  with corporate and IT strategies.
• Expert proficiency in programming tools (such as Python, R, etc) for data manipulation, statistical analysis, and machine learning tasks is essential.
• Demonstrate a profound command over  computer science  fundamentals, encompassing expert-level knowledge of data structures, algorithms, computability and complexity, and computer architecture.
• Demonstrate a strong understanding of applications and machine learning algorithms, aligned to best practices globally.
• Spearheading and guiding the software engineering and design facets of projects, while providing mentorship and fostering collaboration among cross-functional teams.
• Utilize machine learning algorithms and libraries effectively, following established best practices and guidelines.
• Communicate technical concepts effectively to diverse audiences, adapting explanations for non-programming experts.