- Collaborate with business teams to understand the business problems and expectations, and identify potential use cases that can be supported by data science/advanced analytics.
- Define detailed scope for identified use cases, size the business impact, propose appropriate modeling techniques, and design a pilot plan to test model performance in a real business environment.
- Establish a data-driven use case evaluation & prioritization framework, including identifying & address the hurdles
- Build a funnel of exploratory initiatives, with well-understood strategic intent (but no immediate ROI requirement) to generate valuable learnings for future business opportunities.
- Lead data scientists/team members in the execution of data-driven use cases and proactively communicate work progress to business/stakeholders.
- Ensure detailed deployment and implementation of the data science project, and deliver end-to-end solutions within the timeline.
- Measure the impact of data-driven use cases by working with business teams and establishing appropriate measurement frameworks/guidelines.
- Regularly control the performance of data science models and set up a procedure/framework for model retraining/switch.
- Ensure all data-driven use cases documentation is up to date and maintains the highest level of quality
- Responsible for managing, coaching, and developing direct reports, as well as ensuring other team members are receiving the support they require to achieve team and company goals and individual performance standards.
- Manage team member and organization development, including performance management, retention, and succession planning.
- Min. Bachelor's degree with background Mathematics / Statistics / Computer Science
- Minimum of 4 years' experience in leading/managing Data Science team, Data Management and Advanced Analytics
- Business Acumen
- Strong analytical thinking
- Problem Solving
- Excellent written and communications skills to report back the findings in a clear, structured manner are required.
- Strong programming and scripting skills (Python, R, Spark…)
- Good understanding of machine learning techniques and algorithms
- Good applied statistics and experimental design skills, such as distributions, statistical testing, control/test planning, etc.
- Good experience with common data science toolkits (Jupyter notebook, Scikit-learn, Pandas, Numpy…)
- Good understanding of big data infrastructure and query languages (HDFS, SQL, Hive…)
- Work with one of the top players in Financial Service industry (Life Insurance)
- Competitive salary & benefit with hybrid working arrangement
Michael Page