
Senior Analytics Strategist - Featurespace
- Cambridge
- Permanent
- Full-time
- Good degree in a scientific or numerate discipline, e.g. Computer Science, Physics, Mathematics, Engineering
- Excellent client facing skills, able to communicate complex analytical concepts to a variety of audiences, especially in a data science context. E.g. the application of practical machine learning algorithms to real-world data
- Ability to understand complex systems quickly
- Strong problem-solving skills (especially in data-centric applications) with motivation to take on novel and challenging problems
- Strong, clear, concise written and verbal communication skills
- Experience with software engineering practices, version control and the Unix -command line
- Strong technical and analytical skills with the ability and enthusiasm to pick up new technologies and concepts quickly
- Ability to manage and prioritise changeable workload ensuring internal and external deadlines are met
- Experience working with customers to gather complex sets of requirements including analytical system design, data integration design and model design
- Depth of experience in stakeholder management and managing customer expectations and common challenges
- Knowledge of Python and familiarity with complex SQL queries
- Industry experience in financial services, fraud and fraud strategy
- Experience in requirements management, business analysis or consulting environment
- Experience in delivery of enterprise software systems into large organisations either as vendor or customer
- Knowledge of fundamental machine learning concepts (feature engineering, algorithms, model evaluation, model bias)
- Experience in deploying statistical models and analytical algorithms in industry
- Practical experience of the handling and mining of large, diverse, data sets
- Basic knowledge of event-driven systems and distributed computing for stateful systems
- Experience managing and developing high performing individuals
- Experience working with model governance bodies or awareness of the issues facing governance of machine learning models in production