Data Scientist

Job Purpose
- The job holder proposes, initiates and manages multiple ML projects together with business in order to address problems raised before linked to company OKRs and product enhancements using DS methods, processes and systems on unstructured, diverse Big Data sources.
- The job holder also participates in strategic decision circles and contributes to guiding business high level and providing strategic data guidance
- The job holder is required to allocate resources, decide strategically on projects and then cascade down to leads.
Job Description
1. Data Solution
- Evaluate effectiveness of proposed models and track business performance KPIs against data model.
- Build cutting-edge algorithms and work with machine learning and deep learning tools to deliver advance analytics solutions across the firm including recommendation engines, customized data models, etc.
- Drive application of machine learning and big data techniques across different journeys and squads.
- Manage, execute, and review complex data science projects in an agile manner and in compliance with internal regulatory requirements.
2. Data Insight
- Lead the identification and interpretation of meaningful and actionable insights from large data and metadata sources together with business partners.
- Review processes and tools designed to monitor and analyze model performance and prediction accuracy.
- Proactively lead discussions in 3+ squads to identify questions and issues for data science
- Collaborate with Data Engineers to build complex, technical algorithms in data analytics software applications to improve work efficiency.
- Know at all times your data (size, average, distributions, outliers, CR, etc) and be able to estimate model output, impact and come up with sanity checks to detect bugs (discrepancies between expectations and results)
3. Project Management
- Own the project, manage POs, keep everyone on track from distractions, aligned towards lowest hanging fruit and KPI
- Manage project conflicts, challenges and dynamic business requirements to keep operations running at high performance.
- Work with team leads to resolve people problems and project roadblocks, conduct post mortem and root cause analysis to help squads continuously improve their practices to ensure maximum productivity.
4. Talent Development - Manage allocated team, focus on retention and growth of the scientists, personal development and KPI - Mentor and coach junior fellows into fully competent Data Scientists. - Identify and encourage areas for growth and improvement within the team.