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Practice interview questions for data scientists
Example Interview Questions for Data Scientists
- Firstly, is there a trade-off between bias and variance?
- Secondly, how can gradient descent be a problem?
- Thirdly, when have you used Exploratory Data Analysis?
- Fourthly, what steps do you take when choosing a Machine Learning model?
- Next, are convolutions best for images?
- Then, are Residual Networks important?
- Also, how does batch normalization work?
- And have you ever worked with an imbalanced dataset?
- Next, what are the highlights of your MSc research? What didn’t work? What else could you have done?
- Then, how have you overcome over/ underfitting your data model?
- What are dimensionality’s dangers?
- Next, how would you run a Principal Component Analysis (PCA)?
- What is data normalization?
- Also, why is dimensionality reduction important?
Data scientist psychometric test practice
Also, welcome to our newest career blog on senior data scientist jobs. In this post, we will focus: firstly on senior data scientidst salaries; and secondly on how to become a data scientist.
|Data science fellow||$100k|
|Data engineering fellow||$100k|
|Consultant data scientist||$120k|
|Senior analytics fellow||$106k|
How to become a data scientist – What does a Data Scientist do?
Data Science is an interdisciplinary field that uses scientific processes and systems to interpret data. According to Wikipedia:
Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
In laymen’s terms, data scientists use data to predict trends or behaviour in an effort to solve business problems.
So what does a day in the life of a data scientist look like?
The day in the life of a data scientist can be very diverse. It usually offers the opportunity for a good work/life balance and core office hours are usually between 8 and 6. Depending on projects and company some out of office hours work might be needed. Remote work and flexible working might also be possible.
It would probably be quite accurate to say that most data scientists spend a lot of their time in meetings. Meeting stakeholders, understanding requirements, discussing with fellow data scientists to find models and explaining results to stakeholders.
Although the majority of their work involves working with data, they also need to be able to communicate with stakeholders and colleagues.
Harvard Business Review’s named data science “the sexiest job of the 21st century” in 2012, turning data science into the thing to be doing.
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Skills and Responsibilities of a data scientist
As pointed out above, data scientists don’t only work with data and interpreting it, but they need to be able to communicate with stakeholders to understand a problem, brainstorm with colleagues to see which methods would be the best to use and then also be able to communicate the results and findings back to said stakeholders. Therefore all of the below skills will also be needed.
- Analytical skills
- Attention to detail
- Math & Statistics
- Programme Language
- Great communication
Senior data scientist Qualifications
A data scientist should at least have a Master’s Degree, with more than 40% in the market currently still having PhDs. A strong knowledge of R programming is also recommended. Most universities have degrees in data science. Other degrees to consider are those associated with computer science and
Salary expectations for a data scientist
According to Glassdoor, the average base salary expectations for a data scientist is around £42 000 per year as of 24 October 2018. Payscale’s salary expectations are at £35 7320. These will, however, vary greatly according to location, company, experience and seniority. Earnings as a data scientist would vary greatly. With bonuses and other benefits, a data scientist at Google is said to earn an average total pay of around $166,750.
Data literacy skills 2020
Data is the fuel of the 4th industrial revolution that we’re experiencing today. Companies are bombarded by data. The data explosion is worthless to companies unless their people have the data skills to extract insights and make better decisions based on the data. There is a big data skills gap in the market at the moment. While not everyone needs to be a data scientist, all professionals should be data literate. The first step to improve data literacy is to be curious, keep asking “why” and own the data you are already exposed to. You should figure out where the data comes from and how to use it.
Finding employment as a data scientist
Women who want to work in this field should visit Women in Technology.
To look for employment as a data scientist, also try these websites:
Junior Data scientist jobs London
Data Scientist Attributes
Just because a candidate has a data science degree or a data science certification doesn’t mean they have good data intuition. Data scientists with this quality are excellent at identifying patterns within sets of structured and unstructured data. The role of data scientists is constantly evolving and they must now understand the needs of customers as well as the needs of their organization. A good interview question that will help you uncover a candidate’s ability to identify data patterns is to ask them to create a quick data visualization. Let them choose whichever programming language they feel comfortable with such as Python or R, and ask them to demonstrate their ability to pick out a key pattern in a small dataset.
Iterative Design Experience
Data scientists need to be able to work as part of a much larger team in order to deliver results. In the world of big data, it’s the data scientists who ask the questions and the data analysts who provide answers. Data scientists then take these results and draw conclusions or insights before deciding upon the next step. This iterative design process is crucial to the success of any IT department, yet not all candidates will have the ability to work in this way.
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While it’s essential to choose a candidate with a data science certification from a reputable data science course in India or elsewhere, you also need someone who enjoys the iterative development process. During the phone screening interview or the technical interview, ask candidates to explain the last project they worked on in detail. How did they address obstacles along the way? How did they work to make improvements? The answers to these questions will help reveal whether they are able to improve products through the process of iterative design.
A skills-based technical interview will tell you whether a candidate has a solid background in data science and big data analytics and should indicate whether they are good at statistical thinking. However, it’s up to the recruiter to check this during the interview stage. While a candidate’s resume may tell you that they have completed a data science course in Hyderabad or Bangalore, it may not give you a good idea of their communication skills. During the interview, ask your candidates how they would resolve a question using statistics. For instance, does the description to every YouTube video contain the word ‘and’? How would they test that? What script would they create? This question will help highlight any candidates statistical thinking ability.
The latest research shows that over 90 percent of data scientists have a Master’s Degree so you can be fairly confident that any candidate who makes it through the screening phase and into the technical interview has a baseline competency in common programming languages such as Python and R. However, while a skills-based assessment will show you candidate’s proficiency in bash/command line, SQL and Java but it won’t tell you how they react to working with new or unfamiliar coding languages. This is known as a ‘hacker’s spirit’: can someone work with unfamiliar codes or formats or even create their own tools when they can’t find a solution?
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The best data scientists have this ‘hacker’s spirit’ and a life-long love of learning. They constantly re-train and learn new coding skills on the job. A good interview task to determine whether a candidate has the willingness to learn new skills is to challenge them to explain or write in plain English how an algorithm or query would work in a coding language that is unfamiliar to them. This task gives you an insight into their ability to think, problem-solve and react to new challenges, just as they might expect to face when they work for you. They might not arrive at the correct answer but you can tell whether they have a ‘hacker’s spirit’ and are ready to face the constantly evolving challenges in your workplace.
This is an essential quality for any data science candidate. They may have completed a data science and big data analytics course at a prestigious university but are they able to use their knowledge to solve real-life problems? Data scientists routinely execute database runs and queries but in order to be successful, they also need to be able to design new ways of architecting queries. After all, if their results simply answer questions that have previously been asked, what new insights will your organization gain? This is where creativity comes in; can a candidate solve a real-life issue?
Key data scientist challenges
Here’s a list of the five most common challenges data analysts report in their day-to-day work. Which of these are all too familiar?
1. “IF MY BOSS ONLY KNEW HOW LONG DATA PREP REALLY TAKES.”
Every minute that passes is another step closer to an outrageous deadline you are frantically trying to meet. Your boss asked for 15 different charts to present at 10 a.m. like you can wave a magic wand and make answers appear. Why don’t executives understand that’s not how it works?
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