Work-life balance, similar to a government job.
Filed with B and C players who rest and vest. Not a performance culture.
Management doesn’t base rewards and promotion on delivering good tech and products to customers.
Leadership of the ML team is good at managing up but doesn’t actually fix issues. They bury problems and keep low performers so they don’t have to deal with the red tape of HR. Maybe management could do useful things, like get us data?
The ML team has no product vision or strategy. They are just order takers.
Fire low performers.
Here's the interview process: 1. Recruiter phone screen 2. Conversation with Hiring Manager 3. Interactive Coding Interview (Python, Data preprocessing) 4. Final round: virtual on-site interviews * Two 1-hour technical interviews * Product M
The interview process consisted of the following rounds: * Screening round (resume and ML basics) * ML coding round * Software system knowledge round * Data manipulation round (pandas) * ML theory round (discussing theory and ML use-cases)
The experience was very smooth. Justin Fiske, my recruiter, reached out to me on LinkedIn. We had a nice chat, and he introduced me to the hiring manager. I had a call with the hiring manager, one phone interview, and one on-site interview. Through
Here's the interview process: 1. Recruiter phone screen 2. Conversation with Hiring Manager 3. Interactive Coding Interview (Python, Data preprocessing) 4. Final round: virtual on-site interviews * Two 1-hour technical interviews * Product M
The interview process consisted of the following rounds: * Screening round (resume and ML basics) * ML coding round * Software system knowledge round * Data manipulation round (pandas) * ML theory round (discussing theory and ML use-cases)
The experience was very smooth. Justin Fiske, my recruiter, reached out to me on LinkedIn. We had a nice chat, and he introduced me to the hiring manager. I had a call with the hiring manager, one phone interview, and one on-site interview. Through