[24]7.AI is a pioneering company in the Conversational AI market, serving over 250 Fortune 500/1000 clients. Renowned for transforming customer interactions through AI and machine learning, [24]7.AI's solutions offer personalized, predictive, and seamless experiences across multiple channels. Their technology assists hundreds of millions of users annually.

As a Data Scientist at [24]7.AI, you will be part of the AI Platform team, focused on building scalable models for conversation automation, workforce engagement, and contact center platforms. The ideal candidate will have strong hands-on skills in machine learning and natural language processing, including experience with Large Language Models (LLMs) and Generative AI. Candidates should hold advanced degrees in AI-related fields and have relevant experience in programming languages like Python, deep learning architectures, and cloud deployment.

In this guide, Interview Query will walk you through the interview process, key responsibilities, and some commonly asked questions for the Data Scientist role at [24]7.AI. 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 [24]7.ai as a Data Scientist. Whether you were contacted by a [24]7.ai 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 [24]7.ai Talent Acquisition Team will make contact and verify key details like your experiences and skill levels. Behavioral questions may also be a part of the screening process.

In some cases, the [24]7.ai hiring manager may be 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 [24]7.ai Data Scientist role usually is conducted through virtual means, including a video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around machine learning algorithms, NLP techniques, and coding skills.

In the case of senior data scientist roles, take-home assignments regarding building ML models, tweaking NLP algorithms, and data analysis tasks are incorporated. Apart from these, your proficiency against 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

Following 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 [24]7.ai office. Your technical prowess, including programming and ML 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 [24]7.ai.

Common Responsibilities by Seniority Level

Data Scientist I

• Implement machine learning models into the products and offerings.
• Create proofs of value of LLM-based solutions for clients' use cases.
• Maintain and refine ML models, ensuring high quality.

Data Scientist II

• Lead the development of machine learning models and their production architecture.
• Define initiatives to establish quick proofs of value for LLM-based solutions.
• Collaborate within the AI Platform Group to review and refine your work.

Data Scientist III and above

• Lead the development and deployment of machine learning models.
• Define and lead the initiatives for Generative AI solutions.
• Mentor a team of data scientists, overseeing work allocation and outcomes.
• Align AI roadmap with leadership goals.

Quick Tips For [24]7.Ai Data Scientist Interviews

• Master Core ML Concepts: Be well-versed in various deep learning architectures such as transformers, autoencoders, and recurrent neural networks (RNNs).
• Showcase NLP Skills: [24]7.ai heavily emphasizes natural language processing (NLP), so proficiency in NLP, NLU (Natural Language Understanding), and NLG (Natural Language Generation) is crucial.
• Possess Cloud Experience: Practical experience in deploying ML models on the cloud (especially Google Cloud and MS Azure) can give you an edge.

Typically, interviews at [24]7.Ai 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.

• Merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: What's the time complexity?

• Find the missing number in an array spanning 0 to n. 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.

• 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).

• 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. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of \(O(\log n)\).

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

• How would you set up an A/B test for button color and position changes? 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 steps would you take if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

• Why might job applications be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them? 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 issues found 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.

• Write a function to calculate sample variance from a list of integers. Create a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example: Input: test_list = [6, 7, 3, 9, 10, 15] Output: get_variance(test_list) -> 13.89

• Is there anything suspicious about the A/B test results with 20 variants? 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 to find the median in a list where over 50% of the elements are the same? Given a list of sorted integers where more than 50% of the list is comprised of the same repeating integer, write a function to return the median value in (O(1)) computational time and space. Example: Input: li = [1,2,2] Output: median(li) -> 2

• What are the drawbacks and formatting changes needed for messy datasets? You have data on student test scores in two different layouts. Identify the drawbacks of the current organization, suggest formatting changes for better analysis, and describe common problems seen in messy datasets. Example 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 compared to 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 [24]7.Ai?

Q: What is [24]7.ai looking for in a Data Scientist?

[24]7.ai seeks highly motivated, skilled individuals with hands-on experience in machine learning, particularly in natural language processing. Candidates should have experience with large-scale ML models, deep learning architectures, and cloud deployment. Strong programming skills in Python, C/C++ or Java are essential, along with good communication and cross-functional collaboration abilities.

Q: What are the primary responsibilities of a Data Scientist at [24]7.ai?

As a Data Scientist at [24]7.ai, you will develop and implement machine learning models, collaborate with engineering and product teams, and contribute to AI initiatives, particularly in Generative AI and Large Language Models. You'll also focus on taking these models to production, ensuring high-quality standards, and leading initiatives for client use cases.

Q: What educational qualifications are required for the Data Scientist position at [24]7.ai?

Candidates should have a Bachelor’s, Master’s, or PhD degree from reputable universities in fields such as Artificial Intelligence, Machine Learning, Data Science, Cognitive Computing, Computational Linguistics, or Computer Science, with a significant focus on machine learning.

Q: How can I prepare for the interview at [24]7.ai?

To prepare for the interview, research the company and its AI initiatives. Practice common technical questions related to machine learning and natural language processing. Utilize resources like Interview Query to hone your skills and practice problem-solving scenarios you might encounter during the interviews.

Q: What makes [24]7.ai a great place to work as a Data Scientist?

Working at [24]7.ai offers the opportunity to be at the forefront of conversational AI and customer engagement solutions. You'll work with advanced AI technologies, contribute to innovative projects, and collaborate with talented teams. The company values creativity, excellence, and the ability to make impactful contributions.

If you want more insights about the company, check out our main 24-7-ai 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 [24]7.Ai'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 24-7-ai 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!