You are looking for a data scientist but don’t know where to start? Most recruiters, especially from non-technical backgrounds, often struggle to hire a technical team for their organization, such as data scientists, iOS developers, Android developers, etc.
But why is it essential to hire a data scientist developer? Data scientists are helpful for businesses to boost revenue by analyzing data to determine product direction or performing machine learning methods on a large scale to increase engagement, retention, and revenue.
In the past few years, the demand for data scientist developers is on the rise. Today, every organization is rushing to hire its first data scientist or build a data science team from scratch. As a result, it leads to an increase in the burden on non-tech recruiters.
If you are the one struggling to hire a data scientist developer, here, we will discuss how to conduct a coding assessment for data science engineer, interviews, screening, and other hiring methods with no or least technical knowledge. Let’s dive in.
Identify Why You Need To Hire A Data Scientist
You can start your hiring process by first understanding why you need to hire a data scientist and what you want them to address. It’s not necessary to have a massive amount of data, but you need some data related to the identified business problems.
If it’s considered too risky, to begin with, you can start by shortlisting and prioritizing your business needs such as:
- Voice-based fraud detection and mitigation in the customer care center
- Product recommendations for an eCommerce website
- Predicting churn for a B2B SaaS company
Identifying the company's needs is important as it can significantly impact the company’s budget.
Preparation for a day before an interview is not about preparing questions in advance and reviewing candidate’s resumes. You need to think out-of-the-box about how you can streamline the hiring process.
- Firstly, you need to prepare a list of technical and soft skills you want in a candidate. Ranking them will be great, and it will help you reflect on what it must have and what is great to have.
- Secondly, you need to have a systematic and structured interview process in which you must include developer skill assessment test, screening, coding test, etc.
- Thirdly, you need to communicate with your team members effectively who will be helping your interview process.
Build A Data-Driven Culture That Favors Data Scientists
It is important to look inwards during the recruiting process and assess if your company culture will work for or against Data Scientists. If your company has no expertise in working with data, your Data Scientist will struggle to convert his or her work into business and customer value.
Also, you need to focus on the overall budget to support a data scientist team. Open-source software and data science tools can bring some of the costs down.
The most important thing to consider is whether a candidate has expertise in mathematics, statistics, programming, computer science, and machine learning.
However, you must not rely on a resume when assessing a candidate’s skills. It’s essential to determine whether a candidate possesses the required skills or not. You can conduct a phone interview, which will help you get a deep knowledge of the candidate’s problem-solving and creative skills.
Note-Taking And Checklists
Suppose you engage your team members to help you in the hiring process. In that case, expectations and good notes must be set before to either confirm or reject the candidate’s expertise to meet the desired needs.
After the interview, it’s helpful when you can assess the fit based on reasoned statements rather than uncertain feelings.
Data scientists are always on demand as businesses face complexities that can only be fixed by effective data analysis. Here are some of the skills you must look for in a data scientist developer.
Fundamentals Of Data Science
First and foremost, you need to consider whether a candidate understands the fundamentals of data science, machine learning, and artificial intelligence. Your data scientist must understand the topics like:
- Difference between machine learning and deep learning
- Common tools and terminologies
- What is supervised and unsupervised learning
- Difference between classification and regression problems
Statistics And Probability
For every data scientist developer, the basic concepts of descriptive statistics like mean, median, mode, variance, the standard deviation is a must. Some of these basic concepts include:
- Statistics for data science: What is Normal Distribution?
- Statistics for Analytics: Hypothesis Testing and Z-Test vs. T-Test
- Statistics of data science: What is Skewness?
Python is one of the most important languages that every data science developer must be proficient in when it comes to programming for Data Science. Along with Python, a developer must also have hands-on experience working with R and Julia.
One of the inherent skills a data scientist developer must have is problem-solving skills. A data scientist you hire must have great skills to address a problem, which means he or she must develop the art of calculating the risks associated with specific business models.
Deep learning is an advanced form of Machine Learning. When hiring a data scientist, you need to identify his/her technical skills. This includes fundamentals of Neural Networks, the library used for creating Deep Learning models like Tensorflow or Keras, and how Convolutional Neural Networks, Recurrent Neural Networks, and RBM and Autoencoders work.
Data Science Tools
A data scientist developer you hire must have hands-on experience with several data science tools such as MS Excel, Python or R, Hadoop, Spark, Tableau, and more.
Hiring a data scientist team can be challenging for recruiters with the least technical knowledge. However, to hire the right fit, you need to avoid the pitfalls such as short-term thinking, poor evaluation criteria, unconscious bias, etc.
With the tips mentioned above, you will be able to hire the right fit for your organization which is vital for the long-term survival of an organization.