Data scientists not only interpret data for organizations, but they build castles out of that data. Their diverse abilities and skills make them a high-demand profession that makes companies get into fierce competition for them. So recruiting can be challenging, especially if you are not a technical recruiter or hiring manager. That’s why I crafted a guide on how to hire a data scientist which will cover also the essential interview questions to ask. So either you’re an employer looking to hire a data scientist or you’re a job seeker, this guide is for you so keep on reading.
Table of contents
What is a Data Scientist?
When hiring a data scientist, you firstly need to understand what this profession is about. This means, be able to differentiate it from other ‘’similar’’ disciplines such as data analysis or data management.
A data analyst is great at reading data; they understand what a company’s data means. However, their role is more about informing on and reading this data rather than structuring it and providing solutions for business decisions. On the other hand, a data manager is an expert in generating and organizing data, but they don’t thoroughly analyze it.
Data scientists, as you might’ve already guessed, can do all this and more. They manage and read data, but they also experiment and use different scientific methods to achieve sustainable growth in companies. They build machine learning pipelines and personalized data products to help businesses understand their customers and make better decisions.
An exciting example of how data scientists help businesses and even sports clubs happened in the early 2000s to the Oakland Athletics football team. Their recruitment budget was so small that they couldn’t recruit quality players. So, the general manager using data science and in-game statics, was able to predict player potential, making the team stronger and better. The results? They make it to the playoffs, and it snowballed from there.
Another crucial difference between a data scientist and other similar professions is that although their foundation is based on their ability to write code, they need social skills to succeed. Data scientists are in constant communication with leaders and business stakeholders that are not necessarily involved in the IT industry. Just think about it this way; what’s the point of making great discoveries when you can’t communicate them and sell your ideas?
Data Scientist Roles and Responsibilities
Data scientists have different roles and responsibilities depending on the industry they are working in. But generally speaking, they are responsible for generating value out of data. This means that they are in charge of structuring and understanding massive amounts of data to provide insights and products. This helps businesses meet their needs, goals and automatize specific processes.
Their main responsibilities include:
- Select features, build and optimize classifiers using machine learning techniques.
- Identify valuable data sources and automate collection processes.
- Undertake data collection, preprocessing, and analysis.
- Analyze large amounts of information to discover trends or patterns.
- Propose solutions for the different challenges businesses face.
- Build predictive models and machine-learning algorithms.
- Present information and insights using data visualization techniques.
8 Interview Questions To Ask Your Data Scientist
The best way to effectively hire a data scientist is to 1) Know exactly what type of candidate you need and 2) Know what their career and role are about. It’s not just about seeing a great resume with solid experience on it, but they should match your expectations and have what it takes to take over your data and make magic with it.
Here are the best data scientist interview questions to test their knowledge and technical abilities.
1. What Are The Differences Between Supervised And Unsupervised Learning?
Supervised machine learning uses known and labeled data as input, and it has a feedback mechanism. The most commonly used supervised learning algorithms are decision trees, logistic regression, and support. On the other hand, unsupervised machine learning uses unlabeled data as input, and it doesn’t have a feedback mechanism. Its most commonly used algorithms are k-means, clustering, hierarchical clustering, and apriori algorithms.
2. Explain The Main Steps In Making A Decision Tree
There are 5 main steps in making a decision tree:
- Take the entire data set as input.
- Calculate entropy of the target variable and the predictor attributed.
- Calculate the information gain of all attributes.
- Choose the attribute with the highest information gain as the root node.
- Repeat the same procedure on every branch until the decision node of each branch is finalized.
3. What Are The Feature Selection Methods Used To Select The Right Variables?
There are two feature selection methods to select the right variables: 1) Filter methods and 2) Wrapper Methods.
Filter methods involve linear discrimination analysis, ANOVA, and Chi-Square. When we’re selecting the features, it’s all about cleaning the data coming in.
Wrapper methods involve forward selection (to test one feature at a time), backward selection (test all the features and start removing them to see what works better), and recursive feature elimination (recursively look at all the different features and how they pair together).
4. What Does p-value Mean?
When you are performing a hypothesis test in statistics, a p-value can help you determine how strong your results are. p-value is a number between 0 a 1 and based on this value you’ll know the strength of the results. For instance:
- Low p-value (≤ 0.05) indicates strong evidence against the null hypothesis which means you can reject the null hypothesis.
- High p-value (≥ 0.05) indicates weak evidence against the null hypothesis, which means you can accept the null hypothesis.
- p-value at 0.05 is considered as marginal; you can both accept or reject the null hypothesis.
5. What Is A Random Forest?
A random forest is a versatile machine learning method that performs both regression and classification tasks. It involves creating multiple decision trees using bootstrapped datasets of the original data and randomly selecting a subset of variables at each decision tree step. The model then chooses the mode of all predictions of each decision tree.
By relying on a majority wins model, reduces the risk of error from an individual tree.
Random forests offer several benefits such as strong performance, non-linear boundaries, cross-validation is not necessary, and gives feature importance.
6. You Randomly Draw A Coin From 100 Coins – 1 Unfair Coin (Head-head), 99 Fair Coins (Heat-tail), And Roll It Times. If The Result Is 10 Heads, What Is The Probability That The Coin Is Unfair?
This can be answered using the Bayes Theorem. The extended equation for the Bayes Theorem is the following:
P(A|B)= P (B|A) P (A)P (B|A) P(A) + P (B| A) P (A)
Assume that the probability of picking the unfair coin is denoted as P(A) and the probability of flipping 10 heads in a row is denoted as P(B). Then P(B|A) is equal to 1, P(B∣¬A) is equal to 0.⁵¹⁰, and P(¬A) is equal to 0.99.
If you fill in the equation, then P(A|B) = 0.9118 or 91.18%.
7. How Do You Handle Missing Data?
To handle missing data, the first step is to determine the percentage of data missing in a specific column. That way, it’s better to choose the appropriate strategy to handle the situation. For example, if most of the data is missing in a column, then dropping the column is the best option unless we have some means to make educated guesses about the missing values.
However, if the data missing is low, there are several ways to fill them up. One strategy is to fill them up with a default value or a value with the highest frequency in that column, such as 0 or 1, etc. Another way is to fill up the missing values in the mean of all the values in that column. This technique is the most popular one as the missing values have a higher chance of being closer to the mean than to the mode.
8. Explain cross-validations
Cross-validation is essentially a model validation technique used to evaluate how the outcomes of a statistical analysis will generalize to an independent data set. It’s mainly used in backgrounds where the objective is to forecast, and you want to estimate how accurately a model will accomplish in practice.
How to Hire a Data Scientist
Recruiting data scientists is hard. A few years ago, the profession of ‘data scientist’ was relatively new. Few companies truly understand what the job was about and if it was important whether to hire them or not. Nowadays, as the world revolves around data, most companies want to hire a data scientist.
According to Glassdoor’s 50 Best Jobs in America report, data scientists are ranked as the second-best job across every industry based on the job opening, salary, and overall job satisfaction ratings.
If you’re looking to hire data scientists but don’t know where to start, here are three strategies that will help you attract and recruit the talent you’re looking for:
1. It All Starts With Your Job Description
Job descriptions are the equivalent of first impressions on blind dates. You go on a date without having a clue of who you’re going to meet, and their first words and looks already help you make up your mind whether you’re going to like the date or not. And job descriptions work exactly the same way.
All job descriptions should be straight to the point and try to avoid weird expressions like ‘’rockstar data scientists.” Trust us, that will likely scare off the most talented ones. It’s always better to keep it simple and concise.
2. Offer Them The Benefits They Really Want
Free snacks, beer pong Fridays, a swimming pool, cool coffee machines, and green areas are amazing benefits that motivate employees. However, is it really what they want? It often happens that employees are excited about working in such places, but after a month or two, that excitement disappears.
Especially after going through a pandemic, where most people loved to work from home, offering those benefits no longer seems enough. Nowadays people are looking for jobs that offer them flexibility so they can choose whether to work from home, at a co-working space, or at the office. Just think about it; they get to work in the environment they are most productive whether this is at the office or at their homes
3. Start Looking Globally
Mistake number three: limiting your options by looking locally. You might be lucky enough to have great talent a few blocks away from you, but honestly, this never happens, especially when we’re talking about data scientists.
Hiring remote data scientists is your chance to bring the best talent to your team. You get to choose from many different countries in the world, and the best of all is that you not only select talented candidates, but you also save money doing so. Costs of living are different in every country!
4. Hire a Remote Data Scientist
If you want to hire a talented remote data scientist at an affordable price and as soon as possible, we’re your best option. At DistantJob, as a boutique remote recruiting agency, we’ve been in the business for more than 10 years. We know where the best IT candidates hide, and we know how to attract them by having the best IT recruitment strategy, so why don’t you leave this to us? Contact us, tell us all about your ideal data scientist, and in less than a month, you’ll be having a fresh new face on your team. And if you are a data scientist looking for a job, feel free to contact us or to check our remote job openings board.