Key Responsibilities

• Apply scientific and technical expertise to produce innovative solutions which will enhance current and future products.

• Define the AI/ML processes to be adopted by the AI team including project inception, exploration, model building, model deployment, and model retraining. Ensure governance of these processes on an ongoing basis.

• Research and evaluate technologies, suppliers, and competitors, to provide recommendations for strategic decisions.

• Document and effectively communicate to the relevant parties the outcomes of the research.

• Protect Intellectual Property arising from the above by supporting the patent process with the IP management department.

• Guide individual team members, provide coordination and support with the planning of their projects and technical consultancy as required.

• Provide team members with required mentoring, and support performance review evaluations.

• Directly support the HR department and Line Manager with recruitment, selection and on-boarding of new team members.

• Collaborate with other teams within R&D to coordinate joint projects, decide on technologies, integrate with new and existing systems, and provide input on AI/Machine Learning topic.

• Represent the company in situations where expertise in AI and data science is required. Liaising with other business functions within the division to support with planning, training, and external representation.

Qualifications: 

• PhD in Mathematics, Computer Sciences, Engineering, A.I. or Physics. 

• Statistics/Programming – Programming skills in languages such as Python, R, SQL, Scala, C/C++, MATLAB and the ability to visualise data, extract insights and communicate the insights in a clear and concise manner.

• Data Science – Expert knowledge of current trends and practices relating to the use of AI, machine learning, data and algorithms. Understanding the data science workflow and recognizing the importance of each element.

• Data Preparation – The ability to deal with data anomalies such as missing values, outliers, unbalanced data and data normalization.

• Model Building – Understanding the use of different methodologies to get insight from the data and translating the insight into business value.

• Model Deployment – The ability to deploy a validated model and monitor it to maintain the accuracy of the results.

• Data Management – Understanding the management of large volumes of structured and unstructured data.