Went through the LinkedIn DS loop a few weeks ago for a mid-level role on the monetization team. Five rounds total. Sharing the breakdown because I spent forever hunting for current info and found mostly 2022 posts.
Phone screen (recruiter): 20 minutes, background + timeline. They wanted to know if I'd worked on products that monetized at scale. Straightforward.
Technical phone screen: 45 min with a DS on the team. One medium SQL problem, one product metric question. The SQL was a self-join to find users active in two consecutive months. Not hard, but you need to write it fast and explain your logic while typing. The metric question was basically: "LinkedIn adds a new 'Share' button to job postings. How do you measure success?" Open-ended. They want to see if you think about guardrail metrics not just north-star.
Onsite (4 rounds): SQL deep dive: two problems back to back, one involving window functions (rank users by connection growth over 30-day rolling windows). The second was a self-referential query on a connections table. Know your window functions cold. Stats / probability: one A/B testing scenario (calculating sample size, discussing novelty effect), one probability question I hadn't seen before involving conditional probability on user behavior. Not leetcode-style, more like graduate stats. Product case: given a metric (feed engagement CTR dropped 10% week over week), walk through your investigation. This round felt most like the job. They really want to see a structured framework: data quality first, then segment, then hypothesize. Cross-functional / behavioral: stories about working with PMs and engineers. How do you communicate a result when stakeholders don't want to hear it.
Overall about 3 weeks from first recruiter contact to offer. Leveling conversations happened after. They're careful about L3 vs L4 and will push back on your self-assessment if they think you're reaching.
Happy to answer questions on any specific round.