Hire data scientists within 4 days

It can be challenging to hire data scientists for your company at times. Although it is currently the most in-demand skill in the industry, finding a skilled data scientist for hire is not as simple as it may appear. That's why we're here to assist businesses looking to hire data scientists on their own.

The process to hire a data science developer can be quite complex, especially if you are not from the relevant technical background. However, Turing provides highly-skilled data scientists for hire, helping you onboard developers within 4 days.

Additionally, if you're from a non-software development background and want to learn more about hiring a data scientist, we've put up an excellent resource for you.

It can be challenging to hire data scientists for your company at times. Although it is currently the most in-demand skill in the industry, finding a skilled data scientist for hire is not as simple as it may appear. That's why we're here to assist businesses looking to hire data scientists on their own.

The process to hire a data science developer can be quite complex, especially if you are not from the relevant technical background. However, Turing provides highly-skilled data scientists for hire, helping you onboard developers in 4 days.

Additionally, if you're from a non-software development background and want to learn more about hiring a data scientist, we've put up an excellent resource for you.

Python is the most common coding language required for data scientist roles. Because of its versatility, developers use this language for most of the steps involved in data science processes. It can take various formats of data, and developers find it easy to import SQL tables into codes with Python. It also allows developers to create datasets, and they can easily source any type of dataset they need on Google. So, when you hire a data scientist, don’t forget to check if they’re well-versed in Python or not.

Although this isn't a definitive requirement, many companies look for candidates with familiarity with Hadoop. Data scientists may encounter a situation where the volume of data they have exceeds their system's memory, or they need to send data to different servers; this is where knowledge of Hadoop comes in handy. They use Hadoop to convey data to various points on a system quickly. Moreover, Hadoop is useful for data exploration, data filtration, data sampling, and summarization. Therefore, if your project is more data-intensive, and you are going to heavily rely on your data scientist for data management, you should pick a candidate who has an in-depth understanding of Hadoop.

Even though NoSQL and Hadoop cover large components of data science, you can still expect a candidate to write and execute complex queries in SQL. SQL is a programming language that helps data scientists carry out operations like adding, deleting, and extracting data from any database. It also carries analytical functions and transforms database structures.

SQL was created to assist businesses in accessing, communicating, and working with a short and large amount of data. When you use it to query a database, it provides you with information. SQL also has short commands that can save you time and reduce the amount of code required to run complex searches, making it an indispensable skill for data scientists. Hence, when you are looking for data scientists for hire, do check their SQL knowledge.

Spark is quickly becoming the most popular big data technology around the globe. Similar to Hadoop, it is a big data processing framework. The sole difference between Spark and Hadoop is that Spark is quicker. This is because Hadoop reads and writes to disc, slowing it down, whereas Spark caches its computations in memory. Apache Spark was created primarily for data science to speed up the execution of complex algorithms. When dealing with a large amount of data, it helps in dispersing data processing and saves time. It also helps data scientists in dealing with large amounts of unstructured data and avoiding data loss. Therefore, if you hire data scientists, gauge their Apache Spark skills well.

The corporate world generates a large volume of data regularly. This information must be converted in a simple to interpret manner. Raw data is more difficult for people to comprehend than images in the form of charts and graphs. Your potential data scientist must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools help data scientists convert complex results from projects to a format that will be easy to comprehend. If you hire data scientists, ensure that they are adept at data visualization to aid your organization in deriving key insights seamlessly.

Often the data a business acquires or receives is not ready for modeling. It is, therefore, important to understand and know how to deal with the imperfections in data. Data wrangling is the process where data scientists prepare data for further analysis, transforming and mapping raw data from one form to another to derive insights. For data wrangling, data scientists acquire data, combine relevant fields, and then cleanse the data. Moreover, this process also enables data scientists to focus more on data analysis rather than the cleaning part and ultimately lead data-driven decision-making in a direction supported by accurate data. Therefore, data wrangling is a significant skill to assess when you hire a data scientist.

A vast number of data scientists lack expertise in machine learning techniques and topics. Neural networks, reinforcement learning, adversarial learning, and other techniques are examples of this. Suppose you want to make your team stand out from others. In that case, you'll need to hire data scientists who are familiar with AI and machine learning techniques like supervised machine learning, decision trees, logistic regression, unsupervised machine learning, time series, natural language processing, outlier detection, computer vision, recommendation engines, and survival analysis, among others.

Creating a hiring funnel will provide you with numerous benefits, like assisting you in identifying the top skills and identifying a Data Scientist who will fit into your company's culture.

Hiring proficient data scientists is key to harnessing the power of data for business success. Here are some sample interview questions to hire data science developers, which you can use to properly evaluate their skills and recruit the ideal candidate.