Clarifai is a leading, full-lifecycle deep learning AI platform specializing in computer vision, natural language processing, LLMs, and audio recognition. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai helps organizations transform unstructured data into structured data at a significantly faster and more accurate rate. The company has raised $100M in funding and continues to grow with employees worldwide.

As a Data Scientist at Clarifai, you will be responsible for developing custom models to solve real-world problems, managing labeled data sets, and supporting client engagements. The role demands expertise in Python, Jupyter notebooks, Mac/Linux environments, and cloud computing (AWS, GCP). You must also reside in the greater DMV area and have a Secret security clearance.

In this guide, Interview Query will walk you through the interview process, commonly asked questions, and valuable tips for this challenging role. Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Clarifai as a Data Scientist. Whether you were contacted by a Clarifai recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Clarifai Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Clarifai Data Scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Clarifai Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Clarifai’s data systems, ETL pipelines, and Python programming.

In the case of Data Scientist roles, take-home assignments regarding model development, performance analysis, and data visualization are incorporated. Apart from these, your proficiency with hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Clarifai office or virtually if required. Your technical prowess, including programming and machine learning modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Scientist role at Clarifai.

Quick Tips For Clarifai Data Scientist Interviews

• Understand Clarifai's Products and Services: Clarifai’s questions can be standardized and heavily rely on situational scenarios involving their products. Study Clarifai’s wide range of AI solutions and understand how you would personally enhance or apply them.
• Brush Up on Technical Skills: Clarifai’s data science interviews assess your ability to provide business-driving insights with AI and machine learning. Brush up on your knowledge of data manipulation, machine learning models, and cloud computing (AWS, GCP).
• Embody the Culture: Clarifai values innovation and has a collaborative, employee-focused environment. Practice responding to behavioral questions with answers that align with Clarifai’s core values and your commitment to advancing AI technology.

Typically, interviews at Clarifai vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

• Write a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Write a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the array. If the value is in the array, return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with the results?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why would the number of job applicants decrease while job postings remain the same? You observe that job postings per day have remained constant, but the number of applicants has been decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common problems in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• How do you write a function to calculate sample variance? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. For example, given test_list = [6, 7, 3, 9, 10, 15], the function should return 13.89.

• Is there anything fishy about the A/B test results? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• How do you find the median in (O(1)) time and space? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in (O(1)) computational time and space. For example, given li = [1,2,2], the function should return 2.

• What are the drawbacks of the given data organization, and how would you reformat it? You have data on student test scores in two different layouts. Identify the drawbacks of the current organization, suggest formatting changes to make the data more useful for analysis, and describe common problems seen in "messy" datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss the advantages of using random forest over logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to evaluate its accuracy and validity?

What is the average salary for a Data Scientist at Clarifai?

Q: What does Clarifai specialize in?

Clarifai is a leading, full-lifecycle deep learning AI platform specializing in computer vision, natural language processing, LLMs, and audio recognition. We transform unstructured images, video, text, and audio data into structured data at a significantly faster and more accurate rate than humans.

Q: What does the Data Scientist role at Clarifai entail?

As a Data Scientist at Clarifai, you will be responsible for developing custom models to solve real-world problems for businesses, managing the development of labeled data sets, analyzing machine learning model performance, documenting your work, and supporting client engagements for creating custom models.

Q: What skills and experiences are required for the Data Scientist position?

You should have experience in machine learning development, proficiency with Python scripts and Jupyter notebooks, experience with Spark SQL and Parquet data, technical writing skills, cloud computing skills (AWS, GCP), and a college degree in computer science, math, or physics. Additionally, you must live in the greater DMV area and hold or recently held a Secret security clearance with the ability to obtain TS/SCI.

Q: What is Clarifai’s company culture like?

Clarifai is proud to be an equal opportunity workplace dedicated to pursuing, hiring, and retaining a diverse workforce. Our culture is innovative and team-oriented, with employees based remotely throughout the United States, Canada, Argentina, India, and Estonia.

Q: How can I prepare for an interview at Clarifai?

To prepare for an interview at Clarifai, research the company's projects and impact in the AI field, review common interview questions on Interview Query, and brush up on your technical skills, especially in machine learning, Python, and cloud computing.

The role of a Data Scientist at Clarifai offers a unique opportunity to develop custom models that address real-world problems, thus solidifying Clarifai's position in the burgeoning AI solutions space. With innovative tasks such as managing labeled data sets and documenting machine learning models, you'll have the chance to make significant contributions while working in a diverse and inclusive environment.

If you want more insights about the company, check out our main Clarifai Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Clarifai’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Clarifai Data Scientist interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!