Machine Learning Engineer ApprenticeScience and Technology Facilities Council • Chilton, England, United Kingdom
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Machine Learning Engineer Apprentice
Science and Technology Facilities Council
- Chilton, England, United Kingdom
- Chilton, England, United Kingdom
À propos
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
Apply machine learning principles and standards such as organisational policies, procedures or professional body requirements
Integrate AI-based approaches, including those provided by third-party vendors' APIs, into existing and new processes
Proactive identification of the potential for automation, for example through AI solutions embedded within tooling
Qualifications
Level 6 Qualification in Machine Learning (Course limited to Level 6 content only; does not include Levels 4 or 5)
GCSE in English (grade 4 or above)
GCSE in Maths (grade 4 or above)
Any subject (grade A-C)
Relevant area (grade Pass or above)
Share if you have other relevant qualifications and industry experience; the apprenticeship can be adjusted to reflect what you already know
Core Skills
Communication skills
IT skills
Organisation skills
Problem solving skills
Presentation skills
Number skills
Analytical skills
Logical thinking
Team working
About UKRI UKRI is an organisation that brings together the seven disciplinary research councils, Research England and Innovate UK. Together, we build an independent organisation with a strong voice and vision ensuring the UK maintains its world‑leading position in research and innovation. Supporting some of the world's most exciting and challenging research projects, we develop and operate some of the most remarkable scientific facilities in the world. We are pushing the frontiers of human knowledge through fundamental research and delivering benefits for UK society and the economy through world‑class research, skills and business‑led innovation.
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Compétences linguistiques
- English
Avis aux utilisateurs
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