Data science is a crucial part of Shopify’s mission to make commerce better for everyone. Data science at Shopify principally includes two types of work: building products directly backed by data (e.g. machine learning models), and listening to their voice to figure out what to build and how to build it. 

Shopify looks more for Data Scientists who have solved challenging problems than those familiar with specific tools or technologies. They are looking for talented data scientists to help them better understand their merchants and buyers.

Shopify hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact.They have Senior and Staff level roles and some openings for Data Science Managers. For instance, there was recently a role in Merchant Services - the goal being to give small businesses targeted advice that can help them better compete with larger ecommerce players. Given the scope and complexity of this undertaking, this was a Staff level role.

As a Data Scientist, you are expected to play a crucial role pushing forward product development across multiple domains. Data Scientists do this by informing high-level product strategy and execution, building a strong and broad operating picture allowing Shopify to move quickly, and/or improving underlying algorithms, heuristics and rules powering individual products - making the Shopify Data Scientist a sought-after role!

How to Apply for a Data Scientist Job at Shopify?

Take a look at Shopify’s website and visit their careers page. You'll find plenty of opportunities along with the Data Scientist role at Shopify, and you can easily apply to roles directly on the site. However, we highly recommend taking the referral route if you know someone in the company as it significantly increases your chances. Before you hit the apply button, make sure you read the job requirements thoroughly. Nothing's more frustrating than getting caught off guard during an interview. If you want to increase your chances even more, tailor your resume to align it with the qualifications and experiences listed in the job posting. It'll make you stand out from the rest. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.

Responsibilities of a Data Scientist at Shopify

The responsibilities of a data scientist at Shopify across roles can broadly be seen as-

Actively seeking out and promoting projects that tackle vital issues.

Utilizing Data Engineering frameworks (such as Starscream and Seamster) to gain insights.

Being sufficiently familiar with BI tools such as Mode, PowerBI, and Tableau - to be able to visualize data in an effective way to drive insights.

Collaborating with teams such as product, engineering, and other business leaders to make data-driven decisions.

Utilizing analytical tools to identify opportunities and guide execution.

Developing actionable KPIs, production-quality dashboards, informative deep dives, and scalable data products.

Influencing leadership to drive more data-informed decisions across the organization.

Skills and Qualifications needed for Data Scientists at Shopify

Here are some skills and qualifications that will help you excel in your Data Science interviews at Shopify.

It's beneficial to have at least 5+ years of experience in Data Science roles, which can help you stand out from other candidates. From what we’ve seen - candidates with less than 5 years of experience often struggle - even to get interview calls; let alone the interview loop.

Brush up on your SQL skills and make sure you have a solid understanding of software engineering fundamentals.

Being proficient in Object Oriented Programming will give you an edge in the interview process. This is because Data Scientists at Shopify are expected to be pretty much “full stack” in nature.

Showcase your experience with applied statistics and your ability to solve quantitative problems.

Be prepared to explain how you've turned analytical insights into clear recommendations, and demonstrate your ability to communicate effectively with technical and non-technical stakeholders.

Highlight your understanding of dimensional modeling and the ability to scale ETL pipelines.

Having experience launching productionized machine learning models at scale will give you an edge over the other candidates.

A Domain experience in e-commerce, marketing or SaaS can help to tailor your approach in a specific industry.

As a part of the Shopify Data Scientist interview, the candidate will need to go through multiple interview rounds:

1. Recruiter call or Behavioural Round - The first round is to have a quick discussion about your work experiences and the roles you’ve had in the past company.

2. Pair Programming or Coding round - The second round is to test your data science concepts and SQL and Python-related concepts. This is to test your analytical skills and to see the approach you take to solve the given problem.

3. The final round of Shopify Data Scientist interviews will usually be a combination of technical rounds and behavioral rounds. You will have a panel of interviewers (often senior data scientists) who will assess you across a range of skills and experiences. The skills you will be likely tested on are: product analytics, metric analysis and behavioral/leadership skills.

A Uber Data Scientist is responsible for analyzing large amounts of data to help the company make informed business decisions. They use a variety of tools and techniques to collect, process, and analyze data from various sources, such as GPS data from Uber's app, customer ratings and feedback, and economic data.

One of the main responsibilities of a Uber Data Scientist is to use this data to identify patterns and trends that can be used to improve the overall performance of the company. For example, they may analyze data on driver and rider behavior to identify areas where the company can improve its matching algorithm or optimize pricing strategies. They also use data to identify new opportunities for growth and expansion, such as identifying cities where Uber is not yet available but where there is a high demand for ride-hailing services.

In addition to analyzing data, a Uber Data Scientist also plays a key role in developing and implementing new data-driven products and features. For example, they may work with engineers and product managers to design and build new data-driven features such as Uber's "destination filter" which allows riders to set a destination before requesting a ride.

Uber hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact. They have Senior and Staff level roles and some openings for Data Science Managers along with some Applied Scientist roles.

Overall, the role of a Uber Data Scientist is to use data and analytics to drive business growth and improve the overall user experience for Uber's riders and drivers.

How to Apply for a Data Scientist Job at Uber?

To apply for a Data Scientist job at Uber, browse the job listings on Uber's career website and find the data scientist position that best matches your qualifications and experience. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. For instance, some DS roles might explicitly call for a background in ML; others might need you to be very good at visualization - these are exactly the sort of things you should then highlight if you have past experience doing. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.

It's important to note that the application process may vary depending on the position and location, so you should always check the specific job listing for more information on the application process.

Responsibilities of a Data Scientist at Uber

The responsibilities of a data scientist at Uber across roles can broadly be seen as-

Creating practical data insights and solutions from Uber’s large datasets.

Employing a mastery of statistical analysis, including descriptive statistics, correlation, regression, and confidence intervals.

Designing metrics and developing SQL queries to generate reports.

Generating ideas for exploratory analysis for a range of use cases. For instance to shape paid marketing strategies.

Use machine learning, experimentation, causal learning, time series analysis, natural language processing, and more to develop and lead statistical and machine learning efforts for different support systems of Uber.

Conducting feasibility studies to analyze the impact of proposed product changes.

Communicating your findings to cross-functional peers and management.

Skills and Qualifications needed for Data Scientists at Uber

Here are some of the skills and qualifications that may be required for a Data Scientist at Uber. One thing to note here is that the degree qualification (bachelor’s/ masters’) is different for every role.

It's mandatory to have at least 5+ years of experience in some Data Science roles.

Make sure you have advanced SQL expertise. This will be crucial for working with the large and complex data sets that are common in this role.

A basic understanding of causal inference, experimental design (such as A/B experiments) and statistical methods will be important for being able to analyze data effectively and guide teams on making data-informed decisions.

Ability to extract insights from data and summarize learnings and takeaways. This will be important for communicating your findings to stakeholders and leadership.

Experience with Excel and some form of dashboarding or data visualization tool (such as Tableau, Mixpanel, Looker, or similar) is a plus as it will help you to effectively present your findings.

As a part of the Uber Data Scientist interview, the candidate will need to go through multiple interview rounds:

1. Phone Screening - The first round is to have a quick discussion about your work experiences and the roles you’ve had in the past company. The standard phone screen is typically taken by a Recruiter from HR.

2. The second round of interviews consists of multiple rounds of interviews with current data scientists from Uber. Each round consists of interviews with Uber staff. Some candidates reported facing more than one interviewer during these rounds. Here you will face questions involving live coding, previous relevant work that you have done and conceptual questions about what Uber does.

3. The third and final round is a virtual onsite interview. This interview is usually with a Hiring Manager. The questions asked here will be related to the case studies to be assessed on the spot.

In this post, we look at interview process for data scientist role at Uber. Interviewing for Uber Data Scientist Profile?

The famous App and social networking platform, TikTok, enables anyone to share their creativity and talent from their phones. It encourages creators to reach a broad audience and allows them to share short videos on its platform.

A data scientist gathers, examines, and interprets vast amounts of data. The position of data scientist evolved from several traditional technical responsibilities, such as those held by mathematicians, scientists, statisticians, and computer specialists.

You can apply for the TikTok Data Scientist job on the career page or get an internal employee reference through Prepfully's Company Referral services.

Identify new product features and evaluate their potential impact with the product team.

Establish product metrics and quantitative measurement structures based on product stages.

Provide insights to internal teams by designing, implementing, and reporting dashboards and data pipelines.

Enhance product adoption and performance-driven growth by partnering closely with key stakeholders.

Provide optimization suggestions to improve operating efficiency by measuring and visualizing the effectiveness of product updates, conducting research, and analyzing various business processes.

Visualize data to understand its benefits and challenges

Understand market solutions on a fundamental level

Analyze data using best practices and techniques

Provide dashboards or self-service applications for sharing results

Experience in working directly with data analysis, processing, and visualization programs

API-based data collection and preparation

At TikTok, data scientists earn between $80,000 and $180,000 annually.

The interview process for the TikTok Data Scientist role consists of 3 stages:

Below is a detailed description of the interview process!

Get TikTok data scientist interview process details in this guide on prepfully.com. Practice interview questions, get tips and other information.

PayPal is one of the largest online payment processors in the world. From fraud detection to product analytics and loss forecasting models, everything at PayPal is data-driven. This is the reason the company is looking to onboard the smartest data scientists to help it grow as a financial services company with technological innovations and data-driven decision making. As a Data Scientist, you will evaluate and improve PayPal's products and services. You'll collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems, and brainstorm solutions to the challenges of measuring quality, preparing models, and improving consumer services in areas such as fraud detection, risk forecasting, consumer analytics and more.

Median salary: USD 160,000 with the base component being USD 100,000, stock component being USD 40,000 and bonus being USD 20,000.

PayPal has a variety of Data Science teams that handle various aspects of the company's data ecosystem, and your exact role may depend significantly on which team you're in. 

Here are some of the Data Science teams at PayPal:

Although each of these roles entails specific responsibilities, a data scientist at PayPal, across the board, is expected to carry out responsibilities such as problem structuring, data preparation, model & strategy building, model & strategy validation, benefits measurement, and more.

Bachelor’s degree in a quantitative or computer science or engineering field with 4+ years professional work experience in a relevant field. Or a Master's degree with 2+ years of professional experience.

Industry experience in payments, e-commerce, or financial services is a strong plus

Strong in data science, analytics and problem-solving. Experienced with predictive modelling, data mining and analytical tools. 

Proficient with SQL, Python, SAS, etc., familiar with Unix.

Proven experience in employing analytics solutions to real-world problems.

Good business sense and logical thinking. Ability to synthesize information and generalize the pattern.

Proven ability to work independently and proficiently on well-defined projects; Focuses primarily on how to achieve overall business objectives of a project with speed and quality

 Effective interpersonal skills. Manage relationships with key partners across different functions. Able to collaborate with remote teams effectively.

Skilled in Presenting – can effectively communicate in large or small group settings.

Once the candidate's resume is shortlisted, the interview process begins, broadly consisting of 2 stages:

Software engineers are computer science professionals using knowledge of engineering principles and programming languages to build software products, develop computer games, and run network control systems.

Zomato is an Indian restaurant search and discovery service founded in 2008. As of now, the company is providing its services in more than 30 countries across the globe.

Zomato hires software engineers to work closely with their teams and contribute to the ever-evolving tech food ordering platform with new features and better user interaction. 

But remember, before you apply for Zomato's Software Engineer job, ensure to have a robust and well-highlighted resume. You can avail of the resume review services at Prepfully to get your resume reviewed by a Zomato recruiter. 

Prepfully gives you a comprehensive interview guide for Zomato's software engineer interview process, job role, interview questions and tips. 

Zomato interview preparation, software engineer job at zomato, Zomato software engineer interview process

A Walmart Data Scientist plays a crucial role in the company's operations by using data to inform and drive business decisions. They use advanced analytical methods to extract insights from large sets of structured and unstructured data, and use these insights to improve performance and drive growth.

One key responsibility of a Walmart Data Scientist is to help the company understand consumer behavior and preferences. They do this by analyzing data from customer transactions, surveys, and social media, and use this information to inform marketing and sales strategies. They also help optimize pricing and inventory management by analyzing data on sales trends, consumer demand, and supply chain logistics.

Walmart hires for Data Scientist roles across the company and offers different levels of seniority depending on the scope and expected impact of the role. They have opportunities for Senior and Staff level positions, as well as openings for Machine Learning and Computer Vision Engineers.

Another important role of a Walmart Data Scientist is to help improve the efficiency and effectiveness of the company's operations. They use data and advanced analytics to identify inefficiencies in the supply chain, and work with other teams to implement solutions. They also use data to improve the performance of the company's website and mobile apps, as well as to inform the design of new products and services.

The examples we’ve outlined above are just a few of the key value-adds that Data Scientists drive at Walmart. There are plenty of others which we haven’t covered here; but we hope this gives you a flavor for what the role could look like.

How to Apply for a Data Scientist Job at Walmart?

Check out Walmart’s career page and browse through the Data Scientist job listings. When you find a role that interests you, be sure to read through the job requirements and qualifications carefully to ensure you meet the criteria. If you have any connections within the company, consider reaching out to them for a referral as it highly increases your chance. When you apply, make sure to tailor your resume to align with the qualifications listed in the job posting. This will help you stand out from other applicants. 

And if you need help with customizing your resume specifically for Walmart (or for that matter, any other company), Prepfully provides resume review services by experienced recruiters in your target company that can give you feedback on your resume. It's worth noting that the application process may vary depending on the position and location, and the company may conduct additional assessments or interviews as part of the selection process.

Responsibilities of a Data Scientist at Walmart

The responsibilities of a data scientist at Walmart across roles can broadly be seen as-

Identifying patterns and trends in complex data sets to inform business decisions.

Using advanced analytics and statistical techniques to analyze data from multiple sources.

Use computer vision techniques to create advanced deep learning models for 2D/3D image generation and synthesis, image classification, scene segmentation, object detection, keypoint detection. For instance, they’ve recently hired a bunch of Computer Vision scientists for their AR/VR explorations.

Perform hands-on modeling and complex analyses using Python, SQL and R.

Communicating insights and recommendations to stakeholders in a clear and actionable manner.

Building and maintaining models to support forecasting, prediction, and optimization (for instance, forecasting demand at a product level).

Continuously monitoring and evaluating the performance of models and data-driven solutions (for instance, in their recommender engine; their sorting engine etc).

Skills and Qualifications needed for Data Scientists at Walmart

Here are some skills and qualifications that will help you excel in your Data Science interviews at Walmart. One thing to note here is that the degree qualification (bachelor’s/ masters’) is different for every role.

It's beneficial to have at least 5+ years of experience in Data Science roles, which can help you stand out from other candidates. Along with this, having previous work experience where you can demonstrate that you’ve used SQL, Python and/or R can also help your profile.

Familiarity with advertising, measurement, and digital marketing analytics can often be a major advantage over other candidates.

Strong coding skills, database knowledge and experience with cloud computing services such as GCP, AWS, or Azure is another thing which can help candidates stand out.

Understanding of causal inferencing, multi-variate testing & design, A/B testing & design, descriptive analytics, and regression analysis is very important for some Data Scientist roles.

Experience with big data technologies  such as Hive, Hadoop, PySpark, BigQuery and modern data visualization tools like Tableau, ThoughtSpot, Looker, PowerBI, etc. can be an advantage.

Knowledge of Machine Learning and Deep Learning libraries like scikit-learn, pytorch, tensorflow, numpy is also something we’ve seen some roles explicitly call out in their requirements.

Experience in statistical methods and advanced modeling techniques (e.g. - SVM, Random Forest, Bayesian inference, graph models, NLP, Computer Vision, neural networks, etc.) along with the optimization and Operational Research techniques can be an added value.

It's important to keep in mind that this list is not exhaustive, and the requirements and qualifications may vary depending on the position and location. It's always best to check the job description and requirements on Walmart's Career page before you apply for the role.

As a part of the Walmart Data Scientist interview, the candidate will need to go through multiple interview rounds. The interview process and questions may differ for different positions and roles.

1. Phone Screening - The first round is to have a quick discussion about your work experiences and the roles you’ve had in the past company. The standard phone screen is typically taken by a Recruiter from HR. However - lots of Prepfully candidates have reported that this is often followed up by the culture fit interview with a Hiring Manager or Senior Manager, right after the Recruiter round. Their view was that the recruiter round was a “Screener” for this Manager round. This seems to be a process unique to Walmart.

2. The second round will comprise multiple technical rounds and it depends on the role and position that you are applying for. Depending on the role and the position, candidates have reported facing technical rounds like ML round, coding questions on HackerRank and even a dataset challenge. Some candidates have also reported working on a take-home case study with a subsequent follow-up interview that expects you to discuss everything you did in the case study, along with being able to reproduce the results.

3. The final round will usually be an interview with a senior data scientist. The questions asked here will be a combination of technical and behavioral questions. The technical questions will be heavily focused on statistics, probability and ML algorithms. On the other hand, the behavioral questions will help the interviewer understand your motivations, background, and ways of working better.

The role of a Data Scientist at DoorDash is to use data to drive decision making and strategy. They collect, analyze, and interpret large sets of data to identify trends and insights that can inform the company's operations and growth.

Data Scientists are embedded across the company in critical roles. For instance - key responsibilities of a Data Scientist working in the Pricing domain at DoorDash include developing and then implementing statistical models which can predict customer behavior and inform pricing strategies. Other Data Scientists working on the Dasher side are responsible for using data to optimize delivery routes and improve the overall efficiency of the company's logistics.

In addition to analyzing data, a Data Scientist at DoorDash also plays a key role in the development and maintenance of the company's data infrastructure. They work closely with the engineering team to ensure that data is properly collected, stored, and protected.

Data Scientists at DoorDash also have to collaborate with cross-functional teams, including product, marketing, and operations, to inform and influence company strategy.

DoorDash hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact. They have Senior level roles and some openings for Data Science Managers along with some Data Analyst and ML Engineer roles.

Note that the availability of these positions is subject to change and may not be available at all times, you could check DoorDash's career page and linkedin page for the latest and more detailed information.

How to Apply for a Data Scientist Job at DoorDash?

To apply for a Data Scientist job at DoorDash, you will need to visit the company's career website and search for open Data Scientist positions. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates.For instance, some DS roles might explicitly call for a background in ML; others might need you to be very good at visualization - these are exactly the sort of things you should then highlight if you have past experience doing. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.

Responsibilities of a Data Scientist at DoorDash

The responsibilities of a data scientist at DoorDash across roles can broadly be seen as-

Design and build data infrastructure that supports DoorDash's data science efforts, including data collection, storage, and protection.

Identify key business metrics and develop reporting and visualization tools to track them.

Develop and build models across a range of use cases. For instance develop and implement statistical models that can predict customer behavior and inform pricing strategies. Optimize delivery routes and improve the overall efficiency of the company's logistics.

Continuously monitor and analyze performance of models, and iterate as necessary.

If you’re interviewing for a Data Science Manager role - you will need to index heavily on the Leadership and People parts alongside core craft skills. You’d be responsible for leading a team of Data Scientists, Data engineers and data analysts - and the end to end strategy and delivery around the data domain you own.

Skills and Qualifications needed for Data Scientists at DoorDash

Some of the skills and qualifications that may be required for a Data Scientist at DoorDash include:

A strong academic background in a field such as computer science, mathematics, statistics, or physics will give you a solid foundation in the concepts and techniques used in data science.

Develop strong coding skills, specifically in Python and SQL. These are the most commonly used languages by data scientists at DoorDash and will be essential for working with large datasets and building models.

Familiarity with statistical and data mining techniques will help you to build and interpret models and make data-driven predictions.

Experience with big data tools like Hadoop, Spark, and Hive. These tools will allow you to work with large datasets and perform complex data analysis tasks.

Knowledge of data visualization tools like Tableau or Power BI will allow you to effectively communicate your findings and insights.

As a part of the DoorDash Data Scientist interview, the candidate will need to go through multiple interview rounds:

1.  HR interview - The first round is an HR interview. This session takes place so that the HR team can better understand your background and can help you understand the role.

2. The second round is a coding round in which you have to solve coding questions related to SQL. This round consists of SQL questions with medium complexity. DoorDash interviewers will often be totally fine with you searching the internet to help guide your answers since this is how work is done in real life. So feel free to check in with them or ask them if you need help and they'll usually give you the go-ahead to do this. In some cases, this round can also consist of a rapid-fire of SQL questions.

3. The third round consists of business intuition interviews. The goal of this round is to understand your depth and breadth of knowledge about DoorDash' business model, and the role you might play within this ecosystem. During the third round, the interviewer will evaluate the candidate's ability to comprehend the given case and meet DoorDash's expectations by asking follow-up questions.

Mock interviews and Resume Review by experienced folks for Data Scientist role at DoorDash. Interview Preparation for DoorDash Data Scientist Interview:

DoorDash Data Scientist Interview Guide - Preparation and Mock Interview

Airbnb is a online marketplace that can be used by people to list or rent short-term lodging. You can book vacation/apartment rentals, homestays, hostel beds etc etc. The company was founded in 2008, and is HQ'd in San Francisco. AirBnB follows the marketplace model - hosts can earn extra income by renting out their homes; travellers get to book unique properties across teh world. Last year, it was used by more than 700 million users.

The roles of data scientists at Airbnb are divided into three segments. Analytics, Inference, and Algorithms

This position entails providing feedback and raising several inquiries. This group of data scientists must be highly meticulous and inquisitive while concentrating on data analysis to find strategic business decisions. 

In this role, problem-solving will involve using statistics and data visualization. Candidates with extensive economics and statistical understanding and backgrounds with doctoral degrees fall into this category.

This position involves maximum programming. Data scientists must work with various computer languages, develop models, and put machine learning systems into use. Ranking suggestions and matching for all users are the issues that data scientists must most frequently deal with. Machine learning engineers are most compatible with this position.

How to Apply for the Data Scientist Job at Airbnb?

To apply for a job at Airbnb, visit the careers page on its website. You should be able to search and apply for roles directly. Naturally, if you know someone in the company - we'd recommend the referral route since this is just so much more effective. In both options; make sure to read the job requirements very thoroughly to avoid "gotcha" moments during the interview. It's also useful to tweak your resume a bit to align it better to the qualifications/experiences required - this just makes it more likely that you'll be invited to an interview. If you need guidance on this, Prepfully has a  resume review service where AirBnB recruiters can give you feedback on your resume.

Get details about the data scientist interview rounds, interview questions and essential tips to crack the interview from Prepfully's  interview guide

data scientist, Airbnb data scientist interview, Airbnb data scientist interview questions

Data is an integral part of a platform like Quora, where millions of registered users look for answers on various domains. At Quora, data scientists work on complex modeling issues for a product with many different content types, users, incentives, and demands. They perform cutting-edge research and analysis that don't result in academic publications, but rather significant product adjustments and paradigm shifts throughout the organization.

The experimentation systems that serve as the cornerstone for all our decision-making are their responsibility to design and put into operation.

Data scientists at Quora need to work on metrics regarding Key Performance Indicators (KPIs) and Objectives and Key Results (OKRs). The goal is to make the platform user-friendly. Quora's data scientist job involves putting effort into products and completing the given data accumulation work. 

Using programming language tools, including SQL, Java, and others, are part of the job. A new joinee in the data science team collaborates with a product management team that shares these objectives and is replete with ex-data scientists. Further, they get training on how to use the scientific method to test more successfully and launch more accurately.

How to Apply for Data Scientist Job at Quora?

You can find the job opening on the official website of Quora or LinkedIn, Indeed, and such portals. Before you apply, you can use Prepfully's resume review services to ensure that your CV is job ready. Read further to prepare better before you appear for a Quora data scientist interview.

Responsibilities of a Data Scientist at Quora

Manage the team of data scientists focusing on driving traffic, keeping high-quality content, and improving the mobile app.

Conduct observational data analysis and prioritize the project according to its importance.

Develop and facilitate experimentation on as well as off platforms.

Work on designing logic for product features, interpret experiments, and help in last-minute product changes.

Do exploratory analysis and understand more about the data.

Create derived features, and data transformations if needed.

Skill/ Qualifications for Data Scientists in Quora

5+ years of experience in data science jobs in top company

Carry a computer science or relevant degree certificate

Using advanced tools to run statistics and do coding

Using programming languages like Python/SQL, Spark / Presto, and others

Statistical competence like A/B Testing, Significance Testing, Regressions Modelling, etc.

Familiarity with qualitative methods of research

As a part of the Quora Data Scientist interview, the candidate will need to go through 3 different rounds:

1. Recruiter call or Phone Screen- The first round is to have a small discussion about your work experiences and the roles you’ve had in the past company.

2. Technical Phone Screen-  The second round is to test your data science concepts and SQL and Python-related concepts. This is to check your primary programming language, use of tools, including ML, familiarity with data visualization, and other areas.

3. The third round, which is also the final round, is the on-site interview. Here the candidate will face senior data scientists. It will involve analytical questions and programming Tests, System Design, Cultural Fit, or the Behavioural Round. The Quora data scientist's salary depends on how well-skilled you are and the experience you carry.

Get details about the data scientist job role and interview process at Quora. Get interview tips and interview questions too. 

A Complete Guide to Quora Data Scientist Interview

GitHub is home to some talented software engineering professionals working together to create some of the most powerful software in the world. Producers and consumers of code, open-source maintainers, security researchers, and security teams share responsibility for the software's security. The main aim of GitHub is to provide the community with the resources it requires to safeguard the software upon which we all rely.

The world's largest developer community, GitHub, has millions of members who collaborate, share code, and create fantastic software.

Their interview approach reflects that their company, based in San Francisco, California, relies significantly on communication, inclusion, and diversity, as 75% of their workers work remotely. Software engineers working with GitHub have solid skills in coding and using tools, including CodeQL. 

The company hires software engineers to work on different tools and roles. Some remote openings are for C++ Compiler, CodeQL Platform, NPM CLI, and others. All under the software engineer position. However, to earn the position, you need to create a good impact on interviewers. A good knowledge of GitHub software engineer interviews will help you pass through the process.

How to Apply for the GitHub Software Engineer Job Role?

To apply for the software engineer role at GitHub, you will find the opening on the official career page of the company website. You can also check the update on LinkedIn and other job portals. 

The role of a software engineer at GitHub depends on the specific position you are applying for. However, the overall role and responsibilities remain the same, except few.

You need to understand and use design patterns to solve business problems.

You need to understand and learn how operating systems work, perform and scale.

Develop pragmatic solutions. Work on building flexible systems without over-engineering and choose simple, straightforward solutions.

Read interview guides for Google, Amazon, Facebook and more.
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