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• Cornell Honours graduate in Statistical Sciences with applied project/team experience in statistical consulting units and hospitality industry • Background in theoretical statistics and applied mathematics with advanced set of programming skills; project management experience handling large data sets – sampling/cleaning, analysing, presenting • Business Analytics 2017 MSc student at Imperial College aiming to pursue career in data science more
I am passionate about Analytics and using statistical techniques to create a data driven organisation. I am familiar with most statistical techniques including Linear and Logistic Regression, CHAID, Clustering, Neural Networks, Association Models as well as NLP for text analysis. I can bridges the gap between the technology, statistics and business. He is also passionate about the value of Social Media data and its value in better understanding of customers.
I have architected and developed SaaS predictive solutions for marketing, experienced in working in an Agile environment and product development.
Skills: R, SQL, SPSS, Tableau
2 of the last organisations I have worked for have been in the Times Tech Track 100 (Black Swan 2017, Portal 2012)
I am a creative and analytical problem solver. Fuelled with a desire for simplicity and evolution. With the goal to use the best technology and ideas to seamlessly enhance daily activities and quality of life for all people.
• 12+ years experience in deriving pragmatic, actionable insight from data, • Advanced analytics, Predictive modelling, Data Visualisation, • Process Automation, Machine Learning, • Locating bottlenecks and optimising business operations, • Strong background in the engineering research and development, • Data lifecycle management (ETL) • Product Optimisation, Design of Experiment using Simulation Tools https://www.linkedin.com/in/kprzysowa/
I help businesses creating value of their data based on state of the art data analysis and modelling techniques. During my PhD, I designed machine learning and data mining tools for the Swiss government. The result are the current national radon map of Switzerland and 5 first author papers on the application of random forest, Bayesian additive regression trees, kernel regression and other data science methods.