Machine Learning Engineer I

The Machine Learning Engineer I is an emerging specialist professional who will kick start their careers by supporting the ML team to apply computer science (including data structures, algorithms, computability and complexity) statistical modeling, and software engineering in machine learning operations (MLOps) to build cutting edge, end-to-end ML data models. The role supports the development of solutions and design of self-running and automated software and predictive models to enable the Group increase efficiencies, reduce costs, identify opportunities that generate value and drive data as a competitive advantage.

Participate in stakeholder meetings and work with senior colleagues to analyze business problems, clarify requirements and define the scope of the resolution needed.

Collaborate within a cross-functional team of Data Scientists, Engineers and Analysts in order to understand project goals, and build, implement and scale-up algorithms for measurable impact.

Display basic understanding of ANN's, CNN's, RNN's, autoencoders, fundamental data science concepts (linear and logistic regression, SVM's, dimensionality reduction), decision trees, gradient boosting, ensemble models, etc. to develop machine learning models.

Work with above architectures within deep learning frameworks such as Keras and TensorFlow.

Demonstrate foundational understanding of relevant applications and/or systems (including, but not limited to, the machine learning algorithms) being created.

Build basic algorithms based on statistical modelling procedures and build and maintain machine learning solutions in production.

Use data modelling and evaluation strategy to find patterns and predict unseen instances.

Train models on large-scale data and fine tune hyper-parameters.

Research 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.

Bachelor’s Degree in Data Science, Computer Science, Mathematics, Statistics, Information Technology, Information Systems or a related field – (essential).

+1 years’ experience working with machine learning frameworks, models or systems with strong mathematical and statistical experience skills - (beneficial).

Exposure to common machine learning, data, math and visualization libraries (i.e. Pandas, pyTorch, SciPy, NumPy, Scikit-Learn etc.) - (essential).

Exposure to developing Machine Learning & NLP solutions over opensource platforms such as (TensorFlow, SparkML, OpenCV, pyTorch, etc.) - (essential).

Exposure to different coding environments (local, notebooks, containers) and software engineering workflows (testing, code management/Git) - (essential).

Proficiency in MS Office 365 with well-developed Excel skills – (essential).

Understanding of relational databases as SQL, MySQL, etc. - (essential)

Familiarity with a cloud environment (AWS, Azure, GCP) and containerized environment (Mesos, Kubernetes, Docker) and CI/CD (Jenkins, AWS Code Pipelines) - (desirable)

Experience in a retail, commercial or IT environment – (desirable).

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. Able to work independently and use own initiative to deal with challenges across a range of problem-solving challenges as and when they arise.

Detailed, organised and quality focused – Has an affinity for detail, structure and efficiency, balancing planning and execution. Is diligent and vigilantly watches over work processes, tasks and outputs to ensure accuracy while independently actioning and correcting any quality concerns.   

Good communication and presentation skills – Ability to communicate effectively both verbally and in writing. Able to explain and simply complex technical concepts and confidently convey information to a variety of stakeholders.

Team player and collaborative partner - Works effectively across functions and as part of a multi-disciplinary team. Is collaborative and able to build sound, professional relationships with internal and external stakeholders.

Ability to work under pressure and under tight time constraints, efficiently prioritising workloads, balancing multiple and competing priorities and managing time effectively in a high-volume, fast-moving environment.  

Is curious and open to learning with a strong interest in data, discovery and trying new ideas. Curious about exploring and answering business analytics questions.