A fast growing global Insurance company is seeking a Data Scientist with hands on experience, responsible for data analytics for Japanese customers in collaboration with the global data team.
The successful candidate will join a newly established team and contribute to a variety of new and exciting, large scale projects with cutting-edge data analytics as well as machine learning technologies.
‐ Think through hard problems, and work with a team to make them reality and provide very tangible benefits to the corporation.
‐ Ensure alignment and prioritization with business objectives and initiatives
‐ Collaborate and socialize key initiatives with product and program managers and other internal stakeholders
‐ Evaluate potential opportunities via customer segmentation, behavior and product analytics
‐ Develop and validate evaluation metrics and models to analyze business performance and areas of opportunity
‐ Rapidly develop proof-of-concept prototypes to prove out hypotheses
‐ Encourage development and sharing of internal best practices and foster cross-departmental collaboration
‐ Ability to work independently, prioritize projects and meet client deadlines
‐ Take ownership of the deliverables and technical document writings
‐ Proven success with generating product strategies using Data Analysis (Big Data, Machine Learning, etc)
‐ General up-to-date knowledge of analytical methods, approaches and recent trends in technologies & machine learning
‐ Engineering / Science / Finance background or 3+ years working experience in those fields
‐ Ability to interpret results using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining independently.
‐ Apache Spark / SQL / R or other technologies used in data analysis
‐ Willing to learn new technologies
‐ Ability to work with cross-functional stakeholders to define and prioritize requirements by drawing out key business insights to provide actionable recommendations.
‐ Experience in raising awareness of BI within market, internally and externally