Summary
Join a fast growing AI research and development company as a senior apprentice. Help shape the AI-driven economy by developing the models that matter, developing specific AI R&D skills. You will study for the level 7 Artificial Intelligence MSc-level qualification.
Wage
£28,000 a year
wage will be reviewed annually
Training course
Artificial intelligence (AI) data specialist (level 7)
Hours
Monday to Friday, 8:30am to 4:30pm, with some flexibility.
37 hours a week
Possible start date
Friday 28 March
Duration
1 year 6 months
Positions available
1
Work
As an apprentice, you’ll work at a company and get hands-on experience. You’ll gain new skills and work alongside experienced staff.
What you’ll do at work
* Design and build novel deep learning frameworks.
* Prepare data to ensure that it is ready to train sophisticated models in the proper manner (removing bias and ensuring data preserves client IP).
* Integrate and test output models: Ensuring that developed models meet required specifications.
* Construct working inference frameworks.
* Conduct ongoing research and replication of the latest AI breakthroughs.
Where you’ll work
Ascend Coworking
Chatham
ME4 4HY
Training
An apprenticeship includes regular training with a college or other training organisation. At least 20% of your working hours will be spent training or studying.
College or training organisation
TECHNICAL PROFESSIONALS LIMITED
Your training course
Artificial intelligence (AI) data specialist (level 7)
Equal to master’s degree
Course contents
* Use applied research and data modelling to design and refine the database & storage architectures to deliver secure, stable and scalable data products to the business
* Independently analyse test data, interpret results and evaluate the suitability of proposed solutions, considering current and future business requirements
* Critically evaluate arguments, assumptions, abstract concepts and data (that may be incomplete), to make recommendations and to enable a business solution or range of solutions to be achieved
* Communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly
* Manage expectations and present user research insight, proposed solutions and/or test findings to clients and stakeholders.
* Provide direction and technical guidance for the business with regard to AI and data science opportunities
* Work autonomously and interact effectively within wide, multidisciplinary teams
* Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers with conflicting priorities, interests and timescales
* Manipulate, analyse and visualise complex datasets
* Select datasets and methodologies most appropriate to the business problem
* Apply aspects of advanced maths and statistics relevant to AI and data science that deliver business outcomes
* Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process
* Identify appropriate resources and architectures for solving a computational problem within the workplace
* Work collaboratively with software engineers to ensure suitable testing and documentation processes are implemented.
* Develop, build and maintain the services and platforms that deliver AI and data science
* Define requirements for, and supervise implementation of, and use data management infrastructure, including enterprise, private and public cloud resources and services
* Consistently implement data curation and data quality controls
* Develop tools that visualise data systems and structures for monitoring and performance
* Use scalable infrastructures, high performance networks, infrastructure and services management and operation to generate effective business solutions.
* Design efficient algorithms for accessing and analysing large amounts of data, including Application Programming Interfaces (API) to different databases and data sets
* Identify and quantify different kinds of uncertainty in the outputs of data collection, experiments and analyses
* Apply scientific methods in a systematic process through experimental design, exploratory data analysis and hypothesis testing to facilitate business decision making
* Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice
* Apply research methodology and project management techniques appropriate to the organisation and products
* Select and use programming languages and tools, and follow appropriate software development practices
* Select and apply the most effective/appropriate AI and data science techniques to solve complex business problems
* Analyse information, frame questions and conduct discussions with subject matter experts and assess existing data to scope new AI and data science requirements
* Undertakes independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances
* Use applied research and data modelling to design and refine the database & storage architectures to deliver secure, stable and scalable data products to the business
* Independently analyse test data, interpret results and evaluate the suitability of proposed solutions, considering current and future business requirements
* Critically evaluate arguments, assumptions, abstract concepts and data (that may be incomplete), to make recommendations and to enable a business solution or range of solutions to be achieved
* Communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly
* Manage expectations and present user research insight, proposed solutions and/or test findings to clients and stakeholders.
* Provide direction and technical guidance for the business with regard to AI and data science opportunities
* Work autonomously and interact effectively within wide, multidisciplinary teams
* Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers with conflicting priorities, interests and timescales
* Manipulate, analyse and visualise complex datasets
* Select datasets and methodologies most appropriate to the business problem
* Apply aspects of advanced maths and statistics relevant to AI and data science that deliver business outcomes
* Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process
* Identify appropriate resources and architectures for solving a computational problem within the workplace
* Work collaboratively with software engineers to ensure suitable testing and documentation processes are implemented.
* Develop, build and maintain the services and platforms that deliver AI and data science
* Define requirements for, and supervise implementation of, and use data management infrastructure, including enterprise, private and public cloud resources and services
* Consistently implement data curation and data quality controls
* Develop tools that visualise data systems and structures for monitoring and performance
* Use scalable infrastructures, high performance networks, infrastructure and services management and operation to generate effective business solutions.
* Design efficient algorithms for accessing and analysing large amounts of data, including Application Programming Interfaces (API) to different databases and data sets
* Identify and quantify different kinds of uncertainty in the outputs of data collection, experiments and analyses
* Apply scientific methods in a systematic process through experimental design, exploratory data analysis and hypothesis testing to facilitate business decision making
* Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice
* Apply research methodology and project management techniques appropriate to the organisation and products
* Select and use programming languages and tools, and follow appropriate software development practices
* Select and apply the most effective/appropriate AI and data science techniques to solve complex business problems
* Analyse information, frame questions and conduct discussions with subject matter experts and assess existing data to scope new AI and data science requirements
* Undertakes independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances
Your training plan
This training plan has not been finalised. Check with this employer if you’ll need to travel to a college or training location for this apprenticeship.
Requirements
Desirable qualifications
A Level in:
* Maths (grade B)
T Level in:
* Computer science (grade Distinction)
Degree in:
* Computer science / mathematics / physics (grade upper second class)
Let the company know about other relevant qualifications and industry experience you have. They can adjust the apprenticeship to reflect what you already know.
Skills
* IT skills
* Number skills
* Analytical skills