Discuss in an hour on how to predict house price starting with a initial dataset. Back and forth as the discussion go deeper. Discussed model choices, variable selection, and model validation, etc.

OA: Three coding questions (Linear Interpolator, Linear regression of daily temperature by town, Efficient fitting of linear regression) 3 Technical Interviews (1 hour each): 1. Data Analysis; 2. Coding and Algorithms; 3. Domain knowledge for Phd or Core Statistics Final round with 2-3 hiring managers and a senior manager from matched teams: combination of open-ended technical problems and behavioral questions Purely behavioral round with HR team and a recruiter

Three Technical: Data analysis - Approach to open-ended problem o Paper, pen - Ask interviewer if stuck - Systematic manner: o Creative ideas o Technical proficiencies o What sort of datasets o What sort of features o Technical / methodology o Start with linear model first - No code Algorithms - Hackerrank o Short, simple and clean code Core statistics / domain expertise - Bachelor / Masters – core statistics o Linear regression o R^2 o T-stat o How they are used o Statistics foundation - No code Two more rounds of intern / hiring managers: - Mainly technical, some behavioral o Similar to data analysis o More targeted towards their team o Come prepared with questions If well, 30 min technical interview: - Logistical interview 30 min of senior meeting with Two Sigma: - Cultural fit o Behavioral mainly o Maybe 1 technical Team match in the spring for more accurate project matching Tested on foundational knowledge