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Ucsf Interview Questions

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Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Score Based on Review

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Statistics

What are the assumptions of linear regression?

Assumptions of Linear Regression

Multicollinearity in Regression

A/B Testing

Experiment Validity

Network Experiment Design

Product Sense & Metrics

Increasing Group Comments

Vision Setting and Execution Strategy

Describe a data project you worked on. What were some of the challenges you faced?

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

How would you handle the data preparation for building a machine learning model using imbalanced data?

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Tell me about a project in which you had to clean and organize a large dataset.

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Analytics

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

How would you tackle multicollinearity in multiple linear regression?

Let’s say you want to test the close friends feature on Instagram Stories.

How would you make a control group and test group to account for network effects?

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

Network Experiment Design I

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

Network Experiment Design II

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

Network Experiment Design III

Describe a time when you had to define a long-term vision for a project or team and move it from concept to reality.

Interviews for leadership or senior technical roles look for more than just “finishing a project.” Your answer should specifically address:

<ol>
<li>The “Why”: What data or organizational gap necessitated this vision?</li>
<li>The Framework: How did you translate a high-level goal into a roadmap?</li>
<li>The Friction: How did you handle stakeholders or team members who were skeptical of the new direction?</li>
</ol>

How would you interpret coefficients of logistic regression for categorical and boolean variables?

Why is one-hot encoding recommended for categorical variables in logistic regression models?

Why is one-hot encoding recommended for categorical variables in logistic regression models?

Let’s say you have a categorical variable with thousands of distinct values, how would you encode it?

Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?

Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?

How would you build a database for a consumer file storage company like Dropbox?

Note: The database must be able to store many different file type formats and keep updates and history of file changes.

Dropbox Database

Data Modeling

Let’s say you’re an ML engineer at Netflix. You have access to reviews of 10K movies. Each review contains multiple sentences along with a score ranging from 1 to 10.

How would you design an ML system to predict the movie score based on the review text?

Let’s say you work on Facebook Groups. 

How would you go about increasing the number of comments on each Group post?

You are tasked with flagging users who post fake comments on Amazon to then give to the modeling team for further analysis.  

<ol>
<li>How would you initially go about filtering users who post fake comments on Amazon?</li>

<li>Let’s say that the team needs more data points faster. How would you scale out the effort with minimal manual intervention?</li>
</ol>

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

Ucsf Interview Questions

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Ucsf Interview Questions

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Discussion & Interview Experiences

Discussion & Interview Experiences