Associate Director Data Science (actuarial) & Risk Consulting Job in Talent Et Au-dela

We will count on you to: Mine and analyse data to drive optimization and improvement of product development and business strategies Assess the need, effectiveness and discovery of new data sources and data gathering techniques through extensive research Develop custom data models and algorithms from scratch to enhance current value proposition and new product developments Apply state-of-the-art advanced analytics, quantitative tools, and modeling techniques to interpret, make inferences, and offer recommendations based on insights from the data Work in a diverse environment by partnering with various Marsh teams to implement models and monitor outcomes Develop processes and tools to monitor and analyse model performance and data accuracy Build sophisticated risk model and take ownership of the product for various geographies What you need to have: We re looking for someone with 8 - 13 years of experience building Statistical Models and Associated Data Architectures 8+ years P&C Actuarial experience with a pricing background or reserving A degree in Statistics / Mathematics / Actuarial / Computer Science / Engineering Should be on an actuarial track/ or relevant experience in property and casualty insurance with detail understanding of advance modelling techniques. Preferably part-qualified Actuary. Preferable papers (CS1, CS2, SP7, SP8, SA3) Detailed knowledge of actuarial pricing techniques Detail orientation, communication and interpersonal skills Excellent Microsoft Office skills, particularly in Excel (including VBA) Strong problem solving skills with an emphasis on research & product development Knowledge and demonstrated experience of advanced statistical techniques, concepts and applications - Stochastic Modelling, GLM/Regression, Random Forest, Boosting, Trees, Text Mining, Social Network Analysis, etc. Ability to apply and advise on state-of-the-art advanced analytic and quantitative tools and modeling techniques in order to derive business insights, solve complex business problems and improve decisions Expert level proficiency in statistical and computer languages like Python, R, SQL, etc. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks Experience in querying databases A drive to learn and master new technologies and techniques Excellent written and verbal communication skills for coordinating across teams