Lyft Data Scientist interview: probability to causal modeling

I began with a recruiter screen of about 30 minutes. It was structured: they checked the basic boxes and explained what would happen next.

The process then moved through a general data-science screen on probability, A/B testing, and business sense, followed by four deeper technical rounds. Those covered coding and algorithms, optimization, causal modeling, and business acumen tied back to product decisions.

Each round felt like a step up. A timed conditional-probability question made me especially nervous because two people were watching how I reasoned in real time. The rest had a clear theme of statistics-heavy thinking applied to decisions. Preparation helped, but the live problem solving was still difficult.

Location: Toronto, ON. Overall feedback: positive. Offer status: no offer.