People were friendly and mostly organized. The biggest stress was preparing for the case study. You are given a data file to analyze about goods sold in a fictional store, both online and in person and need to discuss your findings. The data file itself isn't too hard, but the panel evaluating your presentation includes director and VP level people, about a dozen or so sitting in.
Perguntas de entrevista [1]
Pergunta 1
For the fictional company in the case study, what is your number one recommendation and why?
Candidatei-me online. O processo levou 2 dias. Fiz uma entrevista na empresa ID Analytics (San Diego, CA).
Entrevista
Two technical prescreen and on-site interview
Hiring process was a little delayed but interviews were on time, professional and well targeted for Data Science position.
Before making a decision I was well informed on expectations and responsibilities, it was important since it was my first job after grad school.
Candidatei-me online. Fui entrevistado pela ID Analytics em mar. de 2017
Entrevista
The interview process was pretty extensive. There were multiple interview rounds.
The first round was a data challenge which tested the candidate's proficiency in using Python or R for data cleaning and preprocessing.
The second round was a phone screen (45 - 60 mints) with a Data Scientist. There were questions about my previous data science related work, basic statistics questions, few questions on software engineering fundamentals, machine learning basics. Overall the difficulty level was moderate. The focus was on checking fundamentals.
The third round was another phone screen (45 - 60 mints) with a Sr. Data Scientist. This round was more technical and mostly the discussion was around machine learning algorithms and their limitations.
Next was onsite at ID's San Diego office. 4 rounds of panel interviews (45 - 60 mints each) with team members and a one-on-one (non-technical) with hiring manager over lunch. All onsite interviews (except with hiring manager) were very technical, focusing on machine learning, programming, statistics, mathematics, map-reduce paradigm, and past work.
Overall my experience was very good. All people I met were very respectful, friendly and courteous. All the interviews were more like discussions rather than typical interrogations. Wherever I got struck I was given hints, which I was able to pick and move ahead.
Tips:
1. Make sure you know everything mentioned on your resume in good detail.
2. Brush up your machine learning and statistics fundamentals.
Perguntas de entrevista [1]
Pergunta 1
Explain how kNN (k nearest neighbor) algorithm works and compute its complexity.