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.
Interview questions [1]
Question 1
predict house price starting with a initial dataset
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
Interview questions [1]
Question 1
First round technical interview: begin with some questions about stats/probability knowledge, and the core is an open-ended case study
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
