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 alignwith 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.
Originally posted on Himalayas