Associate ML Engineer at ShopriteX

Role Purpose  

The purpose of the Machine Learning Engineer I is to apply computer science (including data structures, algorithms, computability and complexity) statistical modeling, and software engineering in machine learning operations (MLOps) to build defined ML data models. The role supports data scientists, analysts and other business functions in driving use-cases and executing modelling requirements to other tribes. The role also provides technical assistance and supports ShopriteX squads with the AWS cloud framework by leveraging the programming, software development and computing expertise inherent to the machine learning engineering function.   

Role Description 

Machine Learning Operations

• Participate in stakeholder meetings and work with senior colleagues to analyze business problems, and design robust and scalable machine learning solutions in accordance with business requirements. 
• Support the Machine Learning Engineering function in designing and developing the ShopriteX-wide mlops process and architecture. 
• Apply a foundational understanding of relevant applications and/or systems (including the machine learning algorithms) being created.  
• Collaborate with architecture, cloud operations and data science teams in advancing the machine learning operations initiative. 
• Apply a foundational understanding of solutions architecting involving the AWS cloud framework and a variety of more sophisticated AWS services including CloudFormation, EC2 and ECR, CodeBuild and IAM. 

Machine Learning Modelling

• Collaborate with a cross-functional team of Data Scientists, Engineers, Analysts, and other business functions in driving use-cases, and provide modelling expertise to other tribes. 
• Apply a foundational understanding of foundational machine learning concepts, time series data, and more complex machine learning concepts such concept drift, dimensionality reduction, practical model update-training, and regularization. 
• Provide technical support to teams with regards to standard machine learning objectives. 
• Support senior colleagues and tribe squads in developing and driving machine learning centric use-cases across ShopriteX. 
• Provide aid to the production of machine learning model artifacts for ShopriteX squads when required. 
• Guide the implementation of more complex machine learning processes such as automated re-training, monitoring concept drift etc. 

AWS Cloud Development

• Provide technical support to ShopriteX squads with work relating to the AWS cloud framework by leveraging the programming, software development and computing expertise inherent to the machine learning engineering function. 
• Support analytics and data science teams with regards to AWS data and compute services such as EC2, EMR and S3. 
• Contribute to the implementation of processes in AWS such as automation, CI/CD patterns and the like. 

Research and Compliance 

• Research and implement appropriate machine learning algorithms and tools and work with senior colleagues to select the correct libraries, programming languages and frameworks for each task.  
• Apply understanding of theoretical frameworks in computer science fundamentals, including data structures, algorithms, computability, complexity and computer architecture.  
• Keep abreast of technological developments in the field and integrate the latest data technologies into existing requirements.  
• Follow best practices and standards of machine learning operations (MLOps) workflows for data preparation, deployment, monitoring and retraining to enable agile application methods to projects, and support machine learning models and data sets within a CI/CD process. 

Qualifications and Experience 

Required 

• Bachelor’s Degree in Data Science, Mathematics, Statistics, Actuarial Science, Computer Science, Machine Learning and AI, Information Systems, Engineering, Software Engineering or a related field – (essential). 
• 0 to 2 years’ experience in a machine learning role or a combination of both statistical modelling and software related roles – (essential). 
• Familiarity in writing code within Linux/Unix environment. 
• Experience working with modern object-oriented programming languages (C++, Java, Python etc) 
• Familiarity with a test-driven development environment. 
• Knowledge of core machine learning concepts such as Boosting, GPU utilization, distributed training and the curse of dim – (essential) 
• Knowledge of popular machine learning and data-centric libraries and modules such as Scikit-learn, NumPy, Pandas etc. – (essential). 

Desirable 

• Knowledge of different machine learning architectures such as Conv.Nets, LSTM’s, XGBoost etc. – (desired). 
• Familiarity with some Deep learning frameworks such as Tensorflow, Pytorch, Keras etc. – (desired). 
• Understanding of the end-to-end machine learning life cycle and machine learning CI/CD operations – (desired). 
• Experience working with large datasets and databases technologies such as PySpark, SQL and Apache Hive – (desired).  
• Knowledge of cloud technology frameworks such as AWS, Azure or GCP – (desired). 
• Knowledge of Agile development – (desired). 

Key competencies and work ethic  

• An emerging programmer with a passion for developing machines and systems and who can learn and apply knowledge with minimal direction.  
• Analytical thinking skills and highly numerate – Able to collect, organise and assimilate disparate, multiple and complex pieces of data to draw sound conclusions and arrive at optimal solutions.  
• Technical aptitude with a passion and excitement for data, new technologies and solutions and its range of possibilities, applications and value for the business.   
• High level of self-motivation and drive to meet and exceed on goals and expectations. 
• Detailed, organised and quality focused – Has an affinity for detail, structure and efficiency . 
• Good communication and presentation skills – Ability to communicate effectively both verbally and in writing. Capable of effectively communicating complex technical concepts. 
• Team player and collaborative partner - Works effectively across functions and as part of a multi-disciplinary team.  
• Ability to work under pressure and under tight time constraints.  
• Curious and open to learning with a strong interest in data, discovery and trying new ideas. Curious about exploring and answering business analytics questions.