Zoom Video Communications, Inc. is an American communications tech company founded in 2011 and headquartered in San Jose, California. The company offers video conferencing and online chat services via a cloud-based software platform that helps transform real-time collaboration experiences. Unlike other competitors, Zoom offers a unified meeting experience with a 3-in-1 platform consisting of HD video conferencing, mobility, and web meetings.
The data science team at Zoom plays an integral role in understanding and improving user experiences, they lie at the foundation of the company’s success. Recently, Zoom announced a “Smart Meeting” feature that offers automatic transcription for its video conferencing services, saving time for users with taking and sharing meeting notes.
To achieve this feat, Zoom employs voice-to-text transcription: a voice-based AI technology that utilizes advanced machine learning technology that can identify the individual voice pattern of every individual in the meeting. As evidenced by this new feature, Zoom is constantly doing ground-breaking work that offers new and even seasoned data scientists a platform at which to grow.
Zoom is a rapidly growing company that relies primarily on data science to make decisions that affect growth, drive innovation, and improve customer experiences. Data scientists, as well as data engineers, data architects, data analysts, and database engineers, play an integral role in maintaining this standard.
Data scientists at Zoom leverage data and data technologies to identify and understand business trends and opportunities for improvement of new and existing products and end-user satisfaction. Even though the company has a central data science team, individual roles and functions may differ slightly, and can be tailored specifically to teams and assigned products/projects. As such, the necessary qualifications can range from standard data analytics and visualization knowledge to machine and deep learning heavy skills.
While Zoom provides a large platform and ecosystem for new data scientists to grow, it is also sought out by highly skilled and experienced data scientists to join the ranks of professionals already making an impact at world scale. On average, Zoom hires experts with at least four years (6+ for senior level) of industry experience working with data to facilitate decisions.
Other relevant requirements include:
The term “data science” at Zoom covers a wide scope of domain expertise, including data scientists, data engineers, and data architects. Although there is an exclusive data science team, data scientists can also be assigned to other internal teams, or collaborate cross-functionally to achieve desired goals. Teams are constantly expanded across the organization, and although general roles may sometimes overlap, primary responsibilities rely heavily on the assigned team.
Below are some of the data science teams at Zoom and their general responsibilities.
The interview process follows the standard tech hiring process. It starts with an initial interview with HR over a Zoom Meeting, and a follow-up take-home challenge will be mailed to you. If you pass the take-home challenge, you will be invited onsite for a series of interviews with the Head of Business, Head of HR, and Data Science Team Lead.
This is a standard exploratory discussion with an HR over Zoom. Throughout the course of the interview, the interviewer will tell you more about the job role, team, and the company as a whole. You also get to discuss your skills and past projects you’ve worked on.
Sample data science interview questions:
After the initial Zoom Meeting, you will be mailed a take-home data challenge that consists mostly of machine learning, data mining, and data structure questions that broadly test your skills. There will be up to 15 questions, with one and a half hours allotted for completion.
The onsite interview is the last stage in the interview process. It comprises three back-to-back interview rounds with the Head of Business, Head of HR, and the Data Science Team Lead. Zoom’s data science onsite interview is a mixture of behavioral and technical interviews, designed to test candidates’ knowledge on the length and breadth of data science, as well as product-sense knowledge.
These are usually case study interview questions with data sets similar to real-life Zoom cases, spanning across basic statistical concepts, machine learning, and designs.
Try this question from Interview Query to practice.
The Zoom data scientist interview follows the standard tech interview process. Questions are standard and tailored-specific to the requirements of individual roles. Interview questions are a mixture of statistics, case-study, coding, behavioural, and product-sense.
Here is an example Python coding test from a Zoom data science interview:
Scan through the following steps and import needed Python libraries– don’t worry, you can import them later if you forget one.
Load the data from the csv file
Perform basic commands to understand the data
Bin the following features:
a) 'currentterm' into [0 to 11], [11 and more] b) 'mrr_entry' into [0 to 14.99], [14.99 to 500], [500 to 5K], [5K and more] c) 'account_age’ into [0 to 90], [90 to 180], [180 to 360], [360 and more] d) 'days_left_in_term’ into [0 to 30], [30 to 360], [360 and more]
Set ‘churn_next_90’ as your target column
Set ‘zoom_account_no’ as an ID column, this should not be a feature
Set ‘ahs_date’ as a date column, this should not be a feature
Treat the binned features from step (4) and the following features as categorical features:
a) 'sales_group', b) 'employee_count', c) 'coreproduct'
Perform feature selection using your preferred method and ML algorithm. Choose 10 features and continue to step (10).
Divide the new data frame (with 10 features) into test and train subset
Use a different algorithm from part (9) and perform cross-validation method for parameter tuning. Print out the results.
Based on results from (11), fit your model on the train subset
Test your fitted model using the test subset
Print feature importance, accuracy score (roc_auc_score), and confusion matrix (crosstab) from step (13)
Save your trained model using pickle
See more Zoom data scientist questions from Interview Query:
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