Machine Learning Engineer (Jnr - Mid) at Progressive Edge

The Machine Learning Engineer 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.

Role Description

• 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.

REQUIREMENTS

• Bachelors Degree in Data Science, Computer Science, Mathematics, Statistics, Information Technology, Information Systems or a related field
• +2 years experience working with machine learning frameworks, models or systems with strong mathematical and statistical experience skills
• Exposure to common machine learning, data, math and visualization libraries (i.e. Pandas, pyTorch, SciPy, NumPy, Scikit-Learn etc.)
• Exposure to developing Machine Learning & NLP solutions over opensource platforms such as (TensorFlow, SparkML, OpenCV, pyTorch, etc.)
• Exposure to different coding environments (local, notebooks, containers) and software engineering workflows (testing, code management/Git)
• Understanding of relational databases as SQL, MySQL

Preferred but not required

• Familiarity with a cloud environment (AWS, Azure, GCP) and containerized environment (Mesos, Kubernetes, Docker) and CI/CD (Jenkins, AWS Code Pipelines)
• Experience in a retail, commercial or IT environment