Associate Data Scientist applicants have rated the interview process at Quantifind with 3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 25% positive. To compare, the company-average is 66.7% positive. This is according to Glassdoor user ratings.
Candidates applying for Associate Data Scientist roles take an average of 21 days to get hired, when considering 4 user submitted interviews for this role. To compare, the hiring process at Quantifind overall takes an average of 28 days.
Common stages of the interview process at Quantifind as a Associate Data Scientist according to 4 Glassdoor interviews include:
Phone interview: 29%
One on one interview: 29%
Other: 14%
Presentation: 14%
Group panel interview: 14%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 3 weeks. I interviewed at Quantifind in Jan 2022
Interview
Applied online --> Recruiter call --> 2 rounds of technical interviews (case study, past experience) --> Onsite (1 round of coding and 3 rounds of manager level interview focusing on problem-solving with lots of stats questions)
Interview questions [1]
Question 1
- What distribution would name (first name +last name) frequency follows?
- If you are to come up with name frequency thresholds for very common/common/rare/very rare, what numbers would you choose?
While the recruiter said the first round would be focused on ML and NLP and would also involve coding in an IDE, the first round followed more of a case study format and involved no coding. The second round consisted of two questions: 1) a leetcode question testing merged intervals, 2) a non-DSA question involving writing pseudo code. Had a highly postiive experience with both interviewers but found the recruiter to be unresponsive and unprofessional. After the final interview, the recruiter stopped responding and I only received an automated rejection email after following up.
Interview questions [1]
Question 1
The case study asked: "Our company delivers insights on negative news to clients by curating a lage-scale news corpus. This corpus consists of millions of documents from various domains in English, with basic metadata available. Recently a client raised concerns that many of the articles we provide-such as gossip, sports, and other non-negative topics-are irrelevant to their needs. To address this feedback, we want to develop a model that automatically filters out these articles. How would you approach designing and implementing such a solution?"
The first interview was a phone call from the recruiter, and the next interview was 45 min interview with Senior Data Scientist about the previous projects and problem solving --> technical interview
I applied online. The process took 3 weeks. I interviewed at Quantifind in Jul 2022
Interview
Phone Screen -> Behavioral -> 3 Technical -> Founder Chat
Coding Technical Interviews (2/3) covered SQL easy-mediums, with some where you would implement the same query but in Python. The other tested ML knowledge using a case study format.
Recruiters were super friendly, helpful, and supportive throughout the entire process! Interviewers were kind and thorough in answering my questions too.
Interview questions [1]
Question 1
A restaurant comes to you for help because of their recent decline in customers. Given text data in the form of Yelp reviews, how would you help a restaurant improve?