Data Scientists possess a wide range of skills which make them highly sought after in a competitive market. Companies are increasingly reliant on their expertise to interpret and utilize data to build complex models. As a non-technical recruiter or hiring manager, it can be difficult to fill these positions.
In order to identify a suitable data scientist for your business in the current climate, you should consider remote hiring as a viable option. This will present you with a larger selection of qualified applicants at a more favorable cost. If you have not yet explored this avenue, it may be worth considering.
This guide has been written to provide employers and potential candidates with the information they need to make the process of hiring a Data Scientist successful. It includes sample interview questions to assist in the process, and is suitable for both employers looking to fill a Data Scientist role, or for those who are applying for the role.
To Begin, Let’s Define “data science.”
It is important to have an understanding of what a data scientist does before making any hiring decisions. It is also essential to be able to differentiate data science from related disciplines such as data analysis and data management.
Data Science builds upon the fundamentals of Data Management and Data Analysis. Techniques from Mathematics, Statistics, Computer Programming, Analytics Reporting, and Artificial Intelligence (AI) / Machine Learning (ML) are employed to extract insights from the data of an organisation, which can aid decision-makers in devising strategies for future growth.
What Can Data Science Do For My Company?
Companies now save a significant amount of data not only because it is required, but also because they actively gather it for potential future use in advertising, personnel selection and other purposes.
Data is essential for companies to be able to operate effectively and efficiently. By collecting, processing and utilising data, businesses can make significant improvements to their operations. Here are some examples of how data science can benefit your organisation:
- Extract useful information for enhancing procedures and methods from the data.
- Analysis of consumer wants and requirements may help develop better products.
- Shareholders should be kept informed of your research results.
Hiring data scientists is, in a nutshell, a way to put data to use for your business.
Who exactly is this Data Scientist and what do they do?
In order to support businesses in developing sustainably, data scientists use data to not only analyse and interpret, but also experiment with new scientific methods. Through the use of machine learning pipelines and customised data products, companies gain the ability to better comprehend their customers and alter their strategies accordingly.
The Competencies of a Data Scientist
Data literacy, data manipulation and data visualisation are essential skills for a data scientist. Consequently, they require specialist knowledge and methods across numerous areas to perform their duties effectively.
- Quantitative Methods and Data Analysis
- Mining For Data
- Detecting Patterns and Making Predictions
- Programming (Java, Python, etc) (Java, Python, etc)
- Devices and Systems for Analyses (Tableau, GoodData, etc)
- Office Equipment (Spreadsheets and Presentations)
The Duties of a Data Scientist
Data scientists are responsible for creating value from data. They are tasked with organising and interpreting large datasets in order to provide insights and solutions that enable businesses to achieve their goals and fulfil their requirements.
Most importantly, they are responsible for:
- Machine learning may be used to choose features, construct classifiers, and fine-tune them.
- Locate sources of useful information and implement automated gathering methods.
- Conduct research, including data gathering, cleaning, and analysis.
- Find patterns or trends by analysing massive data sets.
- Give recommendations for addressing the many problems encountered by companies.
- Create forecasting models and AI algorithms.
- Create a data visualisation to display your findings and ideas.
A Guide to Recruiting Data Scientists
The need for qualified data scientists is increasing in today’s data-driven society. This is a relatively new career path, which has only emerged in the last few years, and many employers were initially unaware of the role or its value. Now, however, businesses are actively seeking to recruit data scientists to take advantage of the opportunities that data offers.
According to the 50 Best Jobs in America study by Glassdoor, Data Scientist has been ranked second in terms of job opportunities, compensation and employee satisfaction.
If you need to fill data scientist positions but aren’t sure where to begin, consider the following three approaches.
1. Refine Your Job Duties
It is essential to create a positive impression on potential employees from the outset, and job postings have a pivotal role in this. A comprehensive and precise job description may encourage suitable candidates to apply for the role.
It is important for job postings to clearly state the duties of the position. Avoid using overly-inflated language such as ‘rockstar data scientists’ as this may deter even the most qualified candidates. We recommend keeping descriptions concise and to the point.
2. Give them what they want most.
Adapting to the needs of your employees and providing the perks they value most can be beneficial. Even the smallest of perks can be appreciated, however they are unlikely to be a major factor when considering whether or not to accept a role within your organisation.
Nowadays, employees have increased expectations for the benefits their employers can provide. Working from home has become increasingly popular in the IT sector in recent years, and many employees value the opportunity to carry out their work in a setting that is most suitable for them. This is beneficial for both the business and the employee.
3. Take a Worldview
If you limit yourself to local opportunities, you may find that the options are limited. It can be a fortunate occurrence to find exceptional talent in close proximity, however this is not a common occurrence amongst data scientists.
Recruiting data scientists remotely offers the potential to access the best talent available. With a diverse and international selection of qualified candidates, this approach also presents the advantage of potentially lower costs due to varying living expenses across different countries.
Here Are Eight Data Science Interview Questions
Data scientists should be tested on their knowledge and skills using these top interview questions.
1. What Sets Supervised Learning Apart from Unsupervised Learning?
Supervised machine learning utilises labelled and known data to learn from its mistakes. Common supervised learning techniques include decision trees, logistic regression and support vector machines. On the other hand, unsupervised machine learning takes data without labels and does not utilise feedback. Popular unsupervised learning techniques include k-means, clustering, hierarchical clustering and apriori algorithms.
2. Walk me through the primary actions of creating a decision tree.
Decision trees are constructed using these five steps:
- Use all of the information available.
- Determine the entropy of the dependent variable and the predictor.
- Determine the total information acquired from all qualities.
- Determine the most informative characteristic to serve as the primary node.
- To ensure that the final decision node is reached at each fork, it is necessary to repeat the aforementioned steps.
3. The right variables must be chosen using a feature selection method; what are these methods?
It’s possible to choose the proper variables using either of two feature selection strategies:
Linear Discriminant Analysis, Analysis of Variance and Chi-Square Tests are all examples of filters that can be used for data cleaning and feature selection. Ensuring the accuracy of incoming data is a priority.
Various wrapping strategies are available, including forward selection (where a single feature is tested in isolation), backward selection (where all characteristics are initially included and then progressively removed to identify the most effective) and recursive feature deletion (where all features and their combinations are analysed).
4. When Used in Statistics, What Does p-Value Mean?
In statistics, a p-value is employed to measure the significance of a hypothesis test. The reliability of results can be estimated by using a p-value which is a numerical value between 0 and 1. For example:
- When the p-value is little (less than 0.05), it signifies there is substantial evidence against the null hypothesis, and you may safely dismiss it.
- If the p-value is high (> 0.05), then there is little reason to reject the null hypothesis.
- At the 0.05 level, it’s hard to say whether to accept or reject the null hypothesis.
5. Define the term “random forest.”
Random Forests can be applied to both regression and classification tasks, making them a versatile machine learning technique. Bootstrapping is used to replicate the original dataset, from which multiple decision trees are then generated with randomly selected variables. The final prediction is taken from the average of the predictions made by all the decision trees.
The potential for inaccuracy from any given tree is mitigated by using a majority-wins approach.
Random forests offer a number of advantages, such as strong performance, non-linear boundaries, no requirement for cross-validation, and the relevance of features.
6. If a coin is chosen at random from a group of 100 coins, and then rolled 10 times with the result being 10 heads, what is the probability that the coin is biased?
The Bayes Theorem may provide us with the solution to this question. Bayes’ Theorem may be written as the following extended equation:
Let’s pretend that P(A) is the probability of choosing the unfair coin and P(A) is the chance of getting 10 in a sequence of heads (B).
P(B | A)= 1
P(B ∣ ¬A) = 0.5¹⁰ = 0.0009765625
The value of P(A) is 0.01.
The value of P(A) is 0.99.
P(A | B) = 0.9118432769, which is less than 91.18 percent if you’re keeping score at home.
7. What Do You Do with Missing Information?
The initial step when addressing missing data is to determine the quantity of information missing from a particular column. Consequently, choosing the most suitable course of action is of paramount importance. For example, if the majority of data in a column is missing, we should likely discard the column unless there is a way to make realistic estimations regarding the missing values.
When the percentage of missing data is small, there are a few options for completing them. One option is to use the most frequent value in that column, or a default value, to fill them in. Alternatively, the median of the numbers in that column can be used to fill any gaps. This method has become widely accepted as missing data tends to be clustered around the mean rather than the mode.
8. Describe the use of cross-validation
Cross-validation is a model validation approach used to assess the generalisability of a statistical model to a new data set. It is primarily employed in contexts where forecasting is the desired outcome, in order to estimate practical results from a model.
Hire a Skilled Data Scientist to Work from Home!
At Works, we have been providing professional remote data science solutions for over 10 years. We offer cost-effective, timely solutions for all your data science needs.
It would be prudent to leave the task of finding the most qualified IT professionals to us. We have the relevant resources and experience to source the right person for the job, as well as the necessary expertise to devise the most effective recruitment strategy. Please get in touch with us and provide details of the data scientist you are looking for; we are confident we can provide you with a suitable engineer within a month.
Feel free to get in touch with us or peruse our remote job postings board if you are a data scientist in need of employment.