Sigmar Recruitment have a great role available with a leading fintech company based in North Dublin.
This is a great opportunity to get involved in a Global project where you will get teh chance to work within a cross-functional team to help grow out a Data Science team!
What you will do:
Apply sophisticated, cutting-edge statistical techniques to very large data sets (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
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
What you will need:
- Bachelor's Degree (minimum 2.1) and proven applied business / non-academic experience.
Knowledge & Experience:
Ideally 2+ years’ experience in an analytical 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
- Previous use of various analytical methodologies, e.g. Random Forest, 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
- Strong technical background; comfortable with programming and / or use of statistical software packages (R, Python, SAS) and Excel
Ability to identify and communicate limitations of tools and methods
Knowledge of data sources, tools and business drivers in insurance industry
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