##Engineer, Data Scientist and (still) actuarial student
A few projects post are posted on the Nyc DataScience Academy website. Code and more recent Machine Learning projects are available on my github.
I studied engineering at EIVP, Ecole des Ingenieurs de la Ville de Paris (Urban Sciences) after three years of intensive maths and physics. Following the path of my grand father, I studied hydraulic and fluids mechanics in particular with a strong interest for modeling and coding. I had a lot of fun during my four internships in the Paris sewage network and diverse water department learning the job. My first full-time job was in a small but great research department in public transportation. The company was among other things in charge of the IT infrastructure to broadcast messages on the highway around Paris. I wrote and created experiments for the message broadcast and bus priority as well as software developments. Besides my Engineering degree, I hesitated between another architecture or finance diploma, I decided to pursue a Master of Corporate finance in EM Lyon (top 4th French Business school). Given a mix of luck and some excellent mentors, I ended up working for several financial institution in Paris and New York where I was in charge of modeling financial and insurance related deals for underwriting or trading purposes. These years were really intense. A lot of hard work and a great start for a career that entitled me with a mix of economics, finance, coding, mathematics and statistics.
Even though my passion lies with mathematics, physics, finance and economics I've recently developed a strong interest for data science and machine learning. During my previous experience, I enjoyed very much working with large spreadsheets, developing financial model cash flows under numerous scenarios and digging into financial statement of insurance companies to understand the behaviors of various financial variables. I also worked with actuaries who seemed to be really comfortable aggregating different concepts into a single model, but now, I see data projects and modeling differently particularly as the scale of the data can be multiplied. After attending a few meetup (Open Data by Vivian and Data Driven by Matt Turk) in New York, I decided to take the actuarial exams to be more comfortable with statistics and probabilities and to take the NYC Data Science 3 months bootcamp during the summer 2015. I am still amazed by all the coding techniques I've learned. All of the possibilities for data and modeling that it opened and Machine learning is also really amazing tool. I can now spend hours and hours trying to figure out how to find the best path using coding and my own intuition to solve larger scale data related problems in a diversified field such as transportation, finance, marketing, advertizing...
Over the last six months, I became addicted to the Kaggle competition (world biggest data science and machine learning online platform) where I ranked 812th over 520,000+ participants: 33rd / 2926 -- BNP Paribas Cardif Claims Management; 40th / 2619 -- Prudential Life Insurance Assessment.
I love DIY, wind surfing, hicking and Kaggle.
Content 2016 Xavier Capdepon