Recently, I've noticed that many of my friends are preparing Lyft（Interviewing at Lyft DS is one of the most important things in a worker's dream! But to be honest, Lyft DS interviews are no easier than Uber or Airbnb, SQL + Analytics + ML + Optimization + Behavioral Interviews in one go, fast paced.
This time to share with you a full-process review, SQL high-frequency test points + field face 6 rounds all organized, save you time to look for information on their own patchwork.
Lyft Interview overview
This Lyft Data Scientist (Data Scientist) onsite interview is the standard6 rounds of VOThe game is 45 minutes per round, with very short intervals and almost no buffer time. The overall feeling I got was:
- Extremely broad coverage of topics: SQL, Machine Learning, Coding, Optimization, Statistical Inference, Business Case, full coverage of behavioral aspects.
- Algorithms & Decision-making are not separate: Many problems are not purely computational, but are also coupled with business scenarios that say logic
- Fast-paced + in-depth questioning: At any time in the middle of the answer, you may be asked to explain the details of the derivation or to give alternatives.
- High risk of chokepoints: If you get stuck for more than 10 seconds, the interviewer will follow up or jump to the next question.
Distribution of interview questions
- Business Case - Structured analysis of business scenarios, KPI design, metrics splitting
- Machine Learning - Full-flow modeling + eight-issue interleaving
- Coding - Decision Tree Implementation (Feature Splitting & Tree Construction)
- Optimization - A solution method combining the advantages and disadvantages of the simplex and interior point methods
- Statistical Inference - A/B Test Derivation Problems, Deriving Formulas
- Hiring Manager - Behavioral aspects: cooperation, conflict, progress management
Difficulty
- Topics span a wide range of topics and switching ideas is easy to get stuck on
- Details are asked in depth, such as conditions of application of formulas, advantages and disadvantages of methods
- Time is so tight that there's little room for prolonged reflection
SQL section: looks basic, hidden pitfalls
1️⃣ Sectoral Data ComparisonSELECT DISTINCT All went well when checking the departmental counts, but when doing the FULL OUTER JOIN When I did, I initially focused on the departments I was matched to and ignored the ones that null values. The interviewer immediately followed up with, "What do these null values mean?"
I was a little stuck in my head, and Programhelp's voice alert cut right in, "This is the result of data misalignment between the two tables, and the results of the joint table will show missing entries, and business-wise it may be that the department is not registered in the other table."
I immediately added an explanation, instead, for extra points.
2️⃣ Specific Employee Queries
For this Kids department vs Maintenance department hire date question, I started out with > comparison, and as a result, the ALL keyword was missed, resulting in the wrong logic.
Just as I was getting ready to debug, a whispered note came over the headset, "Be sure to hire all Maintenance employees later than their hire date with the > ALL." As soon as I finished the change and ran, the resultant form came out in seconds.
3️⃣ CASE Wage Classification
The categorization statement itself was fine, but the interviewer asked for the number of people in each category to be counted right away, and I almost did it manually.Programhelp reminded me, "Using the GROUP BY Salary_category Direct Statistics." One line of SQL solves it in seconds, avoiding low-level mistakes.
6 rounds of VOs in the field: close calls in every round
1. Business Case
It started off well, but got stuck when it came to KPI design, I had forgotten the framework of dividing into short, medium, and long term metrics. programhelp voice reminded me, "Short term operational metrics first, then long term growth metrics." I immediately picked it up, and the logic flowed quite well.
2. Machine Learning
While going through the ML process, I couldn't think of what to do with the high base class variable for the feature selection piece. The assistant prompted me to "mention target encoding and dimensionality reduction methods." As soon as I said it, the interviewer immediately nodded.
3. Coding
When writing a decision tree, I got stuck at the step where the feature split calculates the information gain, and the program didn't run with the expected result. My helper said, "Check the base of the log function." I was sure I was using the wrong math library, so I changed the code and went straight through.
4. Optimization
Asked if there are any solution methods that combine the advantages of simplex and interior point methods, which I really wasn't prepared for. The assistant reminded, "Mention the strategy of combining hybrid algorithms with modern commercial solvers, such as barrier method + simplex." At least it would appear to be a good idea.
5. Statistical Inference
Asked to write the confidence interval formula, I didn't consider the different cases of known and unknown variance at first. The assistant hinted, "First distinguish between z-test and t-test conditions." Not only is the formula correct, but it also adds more application scenarios.
6. Hiring Manager
When speaking about stakeholder conflict of opinion, my original example didn't have enough flair. The helper reminded, "Tell the outcome of the conflict resolution as well and emphasize the positive impact." The entire story integrity was instantly improved.
Feelings after the interview
If not Programhelp 's remote unmarked voice assist, I would have had at least three rounds of this interview where the tempo fell apart due to jamming.
It's good for:
- Precise alerts at choke pointsIt'll take less than 3 seconds to save your life.
- Don't bother.It doesn't affect my ability to answer in my own language.
- Details Completion, such as the indicator framework, statistical hypothesis testing conditions such as easy to miss the place
Lyft's DS interviews are really an all-rounder game, with SQL, ML, optimization, statistics, and business analytics all required. To take this kind of interview, not only do you have to prepare meticulously, but you also have to be completely unruffled by the pace in the field. If you have a similar technical interview or OA, Programhelp is a real-time, seamless assistant that can help you stabilize your output throughout the process.
