Lead the development of machine learning (ML) algorithms and maximise the use of Python open source frameworks across the organisation. Deliver end to end data science projects involving acquisition, link and analysis of different datasets across the organisation and prioritise the workload enabling delivery within the agreed timescales.
1. Lead the implementation of statistical and machine learning methods on Cloud environments, using Python, R and other data science tools as required to maximise the business outcomes
2. Engage with customers across the organisation to capture the analytical requirements and translate them into applicable machine learning solutions suitable to solve real business problems
3. Own the delivery of the data science work including the acquisition, link and analysis of structured and unstructured data from the organisation’s data sources (i.e. sensory and signalling data) to create rich and comprehensive data analysis for the business
4. Collaborate with product and programme development teams such as data engineers, data architects and programme managers to maximise the quality of data science projects
5. Guide and facilitate the product development workshops, advising programme managers and business stakeholders on the best use of data and analytics in products so they are able to better understand solutions and prioritise the delivery accordingly.
6. Lead on the approach and methodology and organise training and development for data scientists to establish a smooth and reliable delivery process as well as a skilled talent pool
7. Manage relationships and engage with suppliers and software vendors to maximise the outcome of their work to the organisation and advise the business on opportunities and risks to maximise the engagement benefits
8. Collaborate and provide advice to the strategy and architecture team in solution design and tool selection decisions to deliver optimal outcomes
9. Present and demonstrate the value of science and mathematics in problem solving through demonstrations, key deliverables and effective communication of results to key stakeholders
10. Keep up with industry trends and stay alert on latest developments in the area of machine learning, translating these into continuous improvement and innovation for the organisation.
Job Skills, Experience and Qualifications
- Degree in relevant discipline (mathematics, statistics or computer science or similar) or equivalent relevant experience.
- Significant and demonstrable experience in developing machine learning and deep learning algorithms
- Leadership experience preferably, in data science, with strong motivation in coaching and growing teams
- Significant experience in data science programming using different programming languages and include Python and R
- Proven experience in pattern recognition, statistical and predictive modelling using Numpy, Scikt-Learn, TensorFlow, Keras, PyTorch and other data mining frameworks on the Cloud
- In-depth knowledge of SQL programming with strong skills in data modelling and data transformation relational databases such as Oracle, SQL Server and MySQL
- Experience in working in complex cross-functional, multi-stakeholder environments with strong analytical and strong commercial awareness
- Excellent written and communication skills
- Masters in relevant discipline (machine learning, computer science, statistics, mathematical science, operational research or similar) or equivalent experience.
- Domain expert in any transport, retail and telecom data models
- Design and Architecture skills
- Experience in Mobile and Web App development
- Experience in web architecture and software engineering