Job Purpose

Lead the design, development, and implementation of cutting-edge analytic engines and services,

leveraging extensive experience and expertise in machine learning to develop and deploy scalable models, optimize algorithms, and drive data-driven decision-making.

Job Responsibilities

• Develop and maintain a machine learning platform, ensuring it meets the needs of the

community and stakeholders.

• Design and build robust inference systems, such as APIs, batch processing, and real-time

streaming, to facilitate the deployment and utilization of machine learning models.

• Implement MLOps practices to streamline the deployment, monitoring, and management of

machine learning models in production. Automate the end-to-end machine learning pipeline, from data ingestion to model deployment and monitoring.

• Ensure the scalability and reliability of the machine learning platform, addressing performancebottlenecks and optimizing resource usage.

• Utilize big data technologies such as Spark, Ray, and Dask to handle large-scale data processing and distributed computing.

• Leverage GPU acceleration to enhance the performance and efficiency of machine learning models, particularly for deep learning tasks.

• Collaborate with cross-functional teams to integrate machine learning solutions into existing systems and workflows.

• Contribute to the development and maintenance of documentation, tutorials, and guides for the machine learning platform.

• Engage with the data science community, participating in discussions, code reviews, and contributions to foster collaboration and innovation.

• 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 teams expertise.

• Contribute to the creation of value from enterprise-wide data, assisting in the translation of data into meaningful business solutions.

• Experienced in deploying or contributed to deployment of at least one end-to-end data science solution that has yielded significant value in the organization at an enterprise level.

• Contribute to the shaping of the organizations 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.

• 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.

• Stay informed about the latest tools and techniques, engaging in continuous learning to

enhance skills and knowledge.

• Proficiency in cloud computing and hands-on experience with deploying complex data

science projects on cloud platforms.

• Collaborate with the team, sharing ideas and insights while conducting experiments and

researching best practices.

• Seek opportunities for personal growth and development, actively participating in knowledge-

sharing and mentorship.

• Contribute to the achievement of the business strategy, objectives, and values, contributing to

the organizations success.

Essential Qualifications

• Matric / Grade 12 / National Senior Certificate

• Advanced Diplomas/National 1st Degrees

Preferred Qualification

STEM Qualification

• Engineering, Computer Science, Econometrics, Mathematical Statistics, Actuary Science

• Masters or Doctorate will be an added advantage

Certifications

Cloud (Azure, AWS), DEVOPS or Data engineering certification. Any Data Science certification will be an added advantage, Coursera, Udemy, SAS Data Scientist certification, Microsoft Data Scientist.

Minimum Experience Level

• MS/PhD in STEM or related technical discipline

• 7 years’ plus  experience in a data science or software engineering role.

• Deep knowledge of machine learning, statistics, optimization or related field.

• Knowledge of Graph Database technology will be a major advantage

• Excellent written and verbal communication skills along with strong desire to work in cross

• functional teams.

• Attitude to thrive in a fun, fast-paced start-up like environment

Technical / Professional Knowledge

• Data Mining

• Data analysis

• Statistical Analysis

• Supervised Learning

• Big Data Technologies

• Unsupervised Learning

• NLP

• Deep Learning

• Feature Engineering/Selection

• HyperParameter Tuning

• Programming

• Model Deployment/Monitoring

• MLOps

• API Development

• Inference Systems

• GPU Utilization

• Distributed Computing (Spark, Ray, Dask)

• Automation of ML Pipelines

• Scalability and Reliability Engineering

• Cloud Computing

• Documentation and Tutorials Development

• Graph Databases

• PostgreSQL, Redis

Behavioural Competencies

• Decision Making

• Innovation

• Technical/Professional Knowledge and Skills

• Customer Focus

• Applied Learning

• Improvement Continuous Improvement