Centre for Factories of the Future
Applied AI and Data Scientist
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About C4FF
The Centre for Factories of the Future (C4FF) is an independent research and innovation organisation delivering UK and international projects involving artificial intelligence, advanced manufacturing, child-protection technology, sustainability, agriculture and digital decision-support systems.
We collaborate with universities, businesses, manufacturers, technology organisations, public-sector bodies and international partners to develop and demonstrate practical solutions to complex industrial and societal challenges.
Our portfolio includes Innovate UK-supported and international collaborative research and innovation projects, including:
- Lean Optimal, developing AI-supported approaches to improve manufacturing efficiency, reduce material waste and avoidable energy consumption, and support operational decision-making.
- CSAMGuard and CSAMGuard+, developing advanced and responsible technologies to help prevent, detect and disrupt child sexual abuse material.
- FERIDE, applying AI, data analysis, Earth-observation and decision-support technologies to regenerative agriculture, soil management, soil-carbon monitoring and environmental sustainability.
Job Overview
C4FF is seeking an experienced and practically minded Applied AI and Data Scientist to contribute across its portfolio of UK and international research and innovation projects.
The successful candidate will work across C4FF’s priority projects, including Lean Optimal, CSAMGuard+ and FERIDE, as well as other current and future projects requiring expertise in artificial intelligence, machine learning, statistics, optimisation and data-driven decision support.
All these projects are organisational priorities. The allocation of the successful candidate’s time will be determined by project requirements, milestones, available data and delivery schedules. The individual may therefore contribute to several projects concurrently.
We are looking for someone with strong capabilities in artificial intelligence, machine learning, statistics and data analysis, combined with experience applying these capabilities to practical industrial, scientific, environmental or societal challenges.
The successful candidate should be able to work across different application domains, understand unfamiliar datasets and collaborate with subject-matter experts. They will help translate research objectives and partner requirements into robust, explainable, secure and usable AI and statistical solutions.
Applicants are not expected to be established specialists in every C4FF application area. Strong transferable AI and statistical expertise, adaptability and experience working on real-world problems are more important.
Key Responsibilities
AI, Machine Learning and Statistical Development
The successful candidate will:
- Contribute to the technical delivery of UK and EU funded research and innovation projects.
- Manage analytical and technical responsibilities across multiple concurrent projects according to agreed milestones and delivery requirements.
- Design, develop, test and validate AI, machine-learning, optimisation and statistical models.
- Select methods appropriate to each project’s objectives, available data, operational context and intended users.
- Apply statistical methods to data exploration, experimental design, hypothesis testing, uncertainty assessment, model validation and performance evaluation.
- Develop predictive, classification, anomaly-detection, pattern-recognition, optimisation and decision-support models where appropriate.
- Establish reliable baselines, key performance indicators and evaluation methodologies.
- Assess model limitations, uncertainty, bias, robustness, generalisability and operational suitability.
- Ensure that models and analytical results are explainable, reproducible and appropriately documented.
- Compare alternative technical approaches and recommend proportionate solutions, recognising when statistical or rules-based methods may be more suitable than complex AI.
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Data Analysis and Preparation
- Analyse complex real-world datasets from manufacturing, agriculture, environmental monitoring, online safety and other application areas.
- Undertake data cleaning, preprocessing, transformation, feature engineering and exploratory data analysis.
- Assess data availability, completeness, quality, representativeness and suitability for the intended analytical purpose.
- Identify data gaps and help project partners establish appropriate data-collection and data-management approaches.
- Support the design and development of data pipelines, databases and interfaces needed for AI development and validation.
- Work with structured and unstructured data, which may include numerical, time-series, sensor, image, text, geospatial and Earth-observation data.
- Ensure that data processing complies with applicable GDPR, cybersecurity, confidentiality, ethical, safeguarding and project-specific requirements.
Essential Qualifications and Experience
Applicants should have:
- A Master’s degree or PhD in Artificial Intelligence, Machine Learning, Data Science, Statistics, Operational Research, Computer Science, Engineering, Applied Mathematics or a closely related discipline.
- Alternatively, a relevant first degree combined with substantial practical experience in AI, data science, statistics, software engineering or technology innovation.
- At least two years of relevant professional or substantial applied research experience in AI, machine learning, data science, statistics or a closely related field.
- Strong practical experience developing machine-learning, statistical or optimisation solutions using Python.
- Good knowledge of statistical analysis, including model selection, hypothesis testing, experimental design, uncertainty and model validation.
- Experience working with complex real-world datasets, including data cleaning, preprocessing, feature engineering and exploratory analysis.
- Experience using relevant Python libraries and frameworks such as Pandas, NumPy, SciPy, Statsmodels, Scikit-learn, TensorFlow or PyTorch.
- Experience evaluating AI and statistical models using appropriate performance measures and validation methods.
- An understanding of software-engineering principles, including software design, testing, debugging, documentation and version control.
- Experience using Git, GitHub or a comparable collaborative version-control platform.
- Experience applying AI, statistics or data science within industry, an applied research environment or a commercially or socially relevant project.
- The ability to translate business, industrial, scientific or research requirements into practical technical solutions.
- Strong analytical, numerical and problem-solving skills.
- The ability to explain complex technical findings to technical and non-technical stakeholders.
- Strong written communication, documentation and technical-reporting skills.
- The ability to manage responsibilities across multiple projects and application areas.
- The ability to work independently while contributing effectively within multidisciplinary and international teams.
- Excellent attention to detail and a commitment to producing high-quality, secure, well-documented and reproducible work.
- Eligibility to work in the United Kingdom.
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Relevant doctoral research, industrial placements and substantial university–industry projects may contribute towards the experience requirement where they demonstrate practical responsibility, collaboration and delivery.
Desirable Qualifications and Experience
The following would be advantageous:
- Experience working with manufacturing, industrial operations or process optimisation
- Experience working with computer vision, natural-language processing or anomaly detection
- Experience working with agriculture, sustainability, environmental or geospatial data
- Experience with mathematical optimisation, operational research or time-series analysis
- Experience working with SQL, databases and data pipelines
- Experience working with cloud platforms such as Azure, AWS or Google Cloud
- Experience with MLOps, containerisation or model deployment
- Experience working with Large Language Models, Generative AI or Retrieval-Augmented Generation
- Experience with explainable AI, responsible AI or privacy-preserving approaches
- Experience working on Innovate UK, Horizon Europe or other collaborative research and innovation programmes
Applicants are not expected to have previous experience in every C4FF application sector. Strong transferable AI and statistical capability, combined with the ability to understand new domains and work effectively with subject-matter experts, is more important.
Candidate Profile
The ideal candidate will combine technical expertise with practical, commercial and societal awareness.
They will be comfortable investigating unfamiliar datasets, selecting appropriate statistical and AI methods, developing high-quality software and working directly with project partners to understand their requirements.
They should be motivated by the opportunity to apply AI and statistics across a varied portfolio spanning advanced manufacturing, agriculture, sustainability and child-protection technology.
This position may suit:
- An AI or data-science professional with approximately two to five years of relevant experience.
- An MSc graduate with meaningful industry or applied-project experience.
- A PhD graduate with substantial applied research, industrial collaboration or university–industry project experience.
- A university researcher seeking to move into a varied and practically focused innovation role.
- An engineering, manufacturing, operational-research or scientific professional with strong AI and statistical capabilities.
- A technically qualified MBA graduate with relevant AI, analytical and industry experience.
Application Requirements
Applicants should provide:
- A current CV.
- A covering letter explaining their relevant AI, statistical, applied research and industry experience.
- Confirmation of their eligibility to work in the United Kingdom.
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