Machine Learning Engineer Apprentice

Responsibilities

• Communicate and work with fellow team members on a daily and weekly basis
• Take an active role in meetings
• Present progress in slides to update team members at regular intervals, weekly or bi-weekly, both onsite and occasionally in conference calls
• Analyse data in order to design machine learning algorithms
• Write documentation/technical notes to document the design of the algorithm
• Use a variety of tools and technologies and coding language(s) used by the team to develop the machine learning algorithms
• Show initiative especially regarding learning new things
• Participate in the wider department and STFC apprentice training programme
• Work independently at times and ask questions if unsure
• Take responsibility and aim to deliver work of a high standard, assess vulnerabilities of the proposed design to ensure that security considerations are built in from inception and throughout the development process
• Translate business needs and technical problems to scope machine learning engineering solutions
• Select and engineer data sets, algorithms and modelling techniques required to develop the machine learning solution
• Apply methodologies and project management techniques for the machine learning activities
• Create and deploy models to produce machine learning solutions
• Document the creation, operation and lifecycle management of assets during the model lifecycle
• Apply techniques for output model testing and tuning to assess accuracy, fit, validity and robustness
• Assess system vulnerabilities and mitigate the threats or risks to assets, data and cyber security
• Refine or re-engineer the model to improve solution performance
• Apply techniques for monitoring models in the live environment to check they remain fit for purpose and stable
• Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process
• Apply machine learning and data science techniques to solve complex business problems
• Track and test continual learning models
• Analyse test data, interpret results and evaluate the suitability of proposed solutions both new and inherited models, considering current and future business requirements
• Identify, consider and advocate for ML solutions to deliver an environmental and operational sustainable outcome
• Transition prototypes into the live environment
• Complete audit activities in compliance with policies, governance, industry regulation and standards
• Consider the risks with using digital and physical supply chains
• Ensure the model capacity is scaled in proportion to the operating requirements
• Support the evaluation and validation of machine learning models and statistical evidence to minimise algorithmic bias being introduced
• Monitor data curation and data quality controls including for synthetic data
• Identify and select the machine learning or artificial intelligence platform architecture and specific hardware, to contribute to solving a computational problem using allocated resources
• Identify and embed changes in work to deliver sustainable outcomes
• Monitor model data drift, using performance metrics to ensure systems are robust when moving outside of their domain of applicability
• Develop a process to decommission assets in line with policy and procedures; manage current and legacy models in line with industry approaches
• Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances
• Coordinate, negotiate with and manage expectations of diverse stakeholders, suppliers and multi-disciplinary teams with conflicting priorities, interests and timescales
• Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information
• Create and disseminate reports, presentations and other documentation that details the model development to confirm stakeholder approval for handover to implementation
• Comply with equality, diversity, and inclusion policies and procedures in the workplace
• Horizon scan to identify new technological developments that offer increased performance of data products
• Appl…