The Data Scientist will be required to proactively fetch information from multiple data suppliers and then conduct sophisticated and systematic analysis of the data to extract actionable insights.The extracted data must be stored in an appropriate internal / external environment for which a database structure must be expanded as needed. There might also be an element of client engagement needed to communicate strategic results back to the client. The role requires the use of advanced analytics technologies and strong statistical methods, which potentially and over time could include the use of machine learning techniques and the use of predictive modelling. This is a data driven role and a solid understanding of statistics and data wrangling methods is required. There is a further requirement to use software skills. The ability to communicate findings as well as working methods across to both technical and business audiences is very important.
Requirements
- Bachelors degree in sciences or engineering with a strong focus on computer science, statistics, mathematics and / or actuarial sciences
- Fluency in Python, SQL and R
- Beneficial if fluent in any of the following programming languages (C#.Net, C, C++, Visual Basic, SQL)
- At least 3-5 years of experience in a data warehousing, data sciences and/ or modelling environment
- Good understanding of software languages and software infrastructure
- Experience in working with and analyzing data
- Beneficial: Insurance industry experience and actuarial background
- Predicitive modelling skills
- Knowledge of machine learning principles at a high level and ability to implement as and when needed
- Critical analytical thinking and attention to detail
- R, SQL, Python and JavaScript experience. high level of competency required in Python and SQL
- Beneficial to have experience deploying web applications to Azure
- Spark experience
- Version control principles
- Consuming data from either files/databases or online sources
- Valadating data from multiple sources
- Data version control and data intergrity for auditing
- Geo Spatial Analysis
- Using BI tools like PowerBI to give insights into data findings
- Analyze and document processess that translate into deriving value from data
- Understand the insurance operational environment
- Break down insurance processess and document this as a set of business requirements for data representation
- Interpret reinsurance treaties in the context of individual data sets
