Lucid Motors Data Scientist Interview Questions
& Process
Real candidates share what happened, how many rounds they had,
and how the experience turned out.
Based on 16 interview experiences · FREE TO READ
Candidate interview experiences
First-hand accounts from people who interviewed at Lucid Motors.
Lucid Motors
Data Scientist
Mid Easy Positive experience No offer 2 rounds
Interview process
Recruiter call Technical screen
Interview formats
Coding Technical
The interview process began with a recruiter phone call. This was followed by a 1-hour technical screening. The screening included a mix of discussing my background, a coding challenge, and several machine learning questions. I wasn't ready for this combination and performed poorly, knowing I made mistakes early on. I was in a rush due to the layoffs and completely unprepared. The questions were generally basic, except for a LeetCode medium problem. I had solved this type of problem during my graduate job hunt but couldn't recall it after four years in the industry. The other ML questions were standard interview questions related to topics on my resume. I felt the interview was easy, and under normal circumstances, I would have passed easily. However, after the interview, I knew I didn't merit advancing, so it's all good.
Confirmed questions6 questions
- Tell me about one LeetCode medium problem related to strings, parenthesis, or backtracking.
- What are the pros, cons, and comparisons of different supervised algorithms?
- Can you elaborate on advanced deep learning concepts?
Lucid Motors
Senior Data Scientist
Senior Average Negative experience No offer 3 rounds
Interview process
Recruiter call Technical screen Onsite
Interview formats
Behavioral Coding Technical
I applied online, no referral. Signed an NDA so I can't share specifics, but here's the general flow. First, an HR screen asking about me, what I'm looking for, and if I'm okay with 5 days/week RTO in Newark. Then, a tech screen with LeetCode-style coding (hashmap basics, then a tricky two-sum variant), ML theory questions on fundamentals like loss functions, metrics, and statistical concepts, and a resume deep-dive. I got through to the onsite, but they were really bad at communicating what to expect, even when I asked multiple times. I withdrew because I got another offer.
Confirmed questions7 questions
- How would you describe yourself and your career goals?
- What are your motivations and expectations for this role?
- Are you willing and able to return to the Newark office full-time (5 days a week)?
Lucid Motors
Data Scientist
Entry Average Negative experience No offer 2 rounds
Interview process
Recruiter call Technical screen
Interview formats
Coding Behavioral
After an initial screening, I had one interview with a member of the data scientist team. The HR person doing the screening was 30 minutes late and clearly unprepared. After a 30-minute delay, they finally got to the questions they were supposed to ask. Following that, I was invited to an online interview that included a live coding task. The data scientist I spoke with was rude, unprepared, and extremely disrespectful. It seemed like they were multitasking during our conversation, using their keyboard, and often asked me to repeat myself. This was the most unprofessional interview I've ever experienced, and I'm relieved I'm not joining this company. They should seriously consider implementing some ethical guidelines for their interviewing process.
Confirmed questions3 questions
- What are your coding strengths and weaknesses?
- Which programming languages do you know, and what past projects have you used them for?
- Can you tell me about projects where you've utilized your programming language skills?
Lucid Motors Data Scientist Interview Questions
Quoted word for word from Lucid Motors interview reports.
“Solve the Two Sum leetcode problem.”
Read reports →“What are the security implications of the Jeep Cherokee hack?”
Read reports →“How do time series models work?”
Read reports →“Can you explain how bagging and boosting methods differ concerning the bias-variance trade-off?”
Read reports →“What is the LP/MILP formulation for manufacturing optimization?”
Read report →“What are the pros, cons, and comparisons of different supervised algorithms?”
Read report →“What distinguishes traditional ML from other approaches?”
Read report →“Based on data, how would you differentiate between a smooth and a rash driver?”
Read report →“How would you approach modeling a battery using machine learning?”
Read report →Formats, difficulty and experience
Across all 16 Lucid Motors interview reports.