WTW Advanced Analytics Practice

Internel

Externel

IT Infrastructure

OS: Linux (Virtual Box), Windows

Language: Python, R

Virtual Environment: Anaconda

Remote Desktop, Cloud(GCP, Azure, Amazon)

Team building

train 1-2 junior data scientist/apprentics

learning organisation: real projects -> self learning -> experience sharing -> real projects

Internal stakeholders

show&tell: 1/month

Open courses, ML&AI hackthorn

Project Management

end2end Simple& fully automatic& scalable &robust(No IT Involvement) solutions to stakeholders

project version control: GitLab

university platform: stack overflow, share solutions

benchmark: ML (80%) better; premium £, difference (NLP, DL for unstructured datasets: text, images, etc.), How can we differentiate ourselves to our competitors on the market?

Business Development

horizontal: claims, underwriting, pricing, etc.

? vertical: Reinsurance

stakeholders with Emblem&Radar: Python -> pmml -> deployment

stakeholders w/t Emblem&Radar: Python -> Flask (Data Engineer)/RShiny Dashboard(No IT Involvement)

?build new deployment platform: McKinsey, Accenture

End2end cloud deployment: Amazon, GCP -> JP

documentation modeling

Target: Provide Better Solutions Faster & Cheaper

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Cost Control, Customer Centricity