The successful candidates will have a unique opportunity to work on innovative Pricing projects across Europe. They will work closely with experienced Actuaries, Senior Analysts and Data Scientists in a collaborative and challenging environment to assist in making recommendations to support key business objectives. They will provide data analysis, predictive modelling and advanced analytics services to key functions across the organisation.
Key Tasks & Responsibilities:
• Apply sophisticated, cutting-edge statistical techniques to very large data sets in the course of predictive modelling and various analytical projects. Examples of the techniques include Decision Trees, Clustering, GLM, Boosting, Neural Networks, Text based analytics
• Build complex tools and analytical models for use by business operations and/or functional groups (e.g. Pricing, Underwriting, Marketing, Claims etc.).
• Produce meaningful observations and recommendations through synthesizing data from internal and external sources. (E.g. economic, weather, customer sat, social media activity, etc.).
• Present findings / recommendations to management
• Carry out predictive modelling and other analyses to solve business problems.
• Perform data preparation steps, including extraction of target data from multiple databases, integration of multiple datasets, and creation of derived variables, application of business rules, and quality control checks
• Collaborate with other advanced analytics staff to share best practices in predictive modelling and build the advanced analytics community
• Master's/PhD degree in Mathematics, Computing, Statistics, Actuarial or another quantitative field
• For non-Masters’ Degree candidates, Bachelor's Degree (minimum 2.1) and proven applied business / non-academic experience.
Knowledge & Experience:
• Ideally 2+ years’ experience in data science role
• Experience / understanding of using statistical techniques e.g. regression analysis, cluster analysis and dimension reduction.
• Experience with numerical optimisation algorithms and concepts such as gradient descent, simulated annealing, convex optimisation
• Identification of relevant statistical techniques for addressing various problems.
• Previous use of various analytical methodologies, e.g. Random Forest, Gradient Boosting, Neural Networks, K-means clustering, etc.
• Knowledge of big data concepts, strategies, and methodologies
• Previous experience using machine learning and big data products within Amazon Web Service (AWS) is highly desirable
• Interpretation of results mathematically and strategically
• Strong technical background; comfortable with programming and / or use of statistical software packages (Python required, SAS & R advantage) and Excel
• Ability to identify and communicate limitations of tools and methods
• Knowledge of data sources, tools and business drivers in insurance industry
• Preferably understanding of the insurance industry, including external influences and internal functions
• Effective communicator with the ability to generate and present insights in a way that can be clearly understood by both technical and non-technical audiences.
• Able to plan own work to meet deadlines
• Ability to adapt to evolving techniques and changing business challenges
• Actively utilise data quality and data preparation best practices (documentation, GIT versioning, etc)
• Ability to find creative solutions to problems
• Highly motivated to deliver consistent high performance
• Good interpersonal skills, works closely with other individuals / teams to achieve goals.
• Seeks to maximise the benefits of a team focussed organisation and supports collaboration
• Able to collect, analyse and challenge technical information
• Distils complex quantitative and qualitative information into key facts and makes well-reasoned recommendations to management
For more information please contact Paul at 016099404 or firstname.lastname@example.org.
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