Data Science Interns at QuantCo will learn what it takes to create impact from data: identifying the right framework and questions to translate a business challenge into a statistical problem; locating and combining data sources as well as transforming large datasets; running in-depth analyses; building machine learning models that automate large-scale decisions and integrating them into live business processes; and interpreting, visualizing, and communicating results to internal and external stakeholders.
What We Want
A major, minor, or research focus in economics, statistics, mathematics, computer science, engineering, physics, or similar fields at a leading university (B.Sc., M.Sc., or PhD)
In-depth understanding of mathematical and statistical concepts behind most common machine learning techniques and a proven interest in causal inference questions, e.g., A/B tests
Experience in coding and working with data (from a previous research or industry internship or extensive course projects) using a wide range of tools and languages: Python (Pandas, XGBoost/LightGBM), R (dplyr, ggplot2), and git
Ability to see solutions and opportunities that others do not see
High level of commitment and reliability, good balance of pragmatism and perfectionism
Business proficiency in English and conversational proficiency in German for our German and Swiss offices
Bonus if skilled in any of the following tools and/or languages:
SQL
Tensorflow
Keras
Cloud infrastructure
Docker
Lower-level languages like C#/C++/C
What we offer
You will be part of a smart, motivated, and driven team that revels in working on challenging problems and finding novel solutions; values our customers and users; and strives to deliver thoughtful and impactful products. You will be surrounded by people that respect each other and the contributions each of us brings. We offer a generous compensation package that includes a base salary, bonus, relocation and housing support along with additional location-based benefits. We accept internship applications year-round for summer and off-cycle periods.
Comentarios