If you’re in the market for a data scientist, you may be feeling overwhelmed. With all of the talk about big data and data science, it can be difficult to know where to start. But don’t worry, we’re here to help.

The job of a data scientist varies greatly according to an organization, therefore it’s critical to first figure out what you’re looking for. Some businesses seek candidates that have excellent data analysis and programming skills; others are searching for business and product insight, and others are looking for both.

What Is A Data Scientist? And What Do They Do?

In essence, a data scientist knows how to interpret data and use it to solve business problems. They have both the technical skillset and business acumen necessary to take data from raw form and glean insights that can improve a company’s bottom line.

Data scientists come from all different backgrounds; many are mathematicians, statisticians, computer scientists, or engineers. But because data science is such a new and nebulous field, many companies are also willing to consider candidates with other degrees such as marketing or finance.

Why Hire A Data Scientist?

There are a variety of reasons why businesses need data scientists and are prepared to pay highly for them.

Some data scientists are hired to clean and organize data so that it can be used by the company. This may include data entry, data mining, data analysis, and creating models to predict future trends.

Other data scientists are brought in to help make better business decisions. They use their knowledge of statistics and machine learning algorithms to find correlations in data and then suggest actions that could improve profits or reduce costs.

Finally, many data scientists are employed specifically for their ability to create visualizations that can help explain complex concepts or findings in a way that is easy for non-technical stakeholders to understand.

Top Industries Hiring Data Scientists The Most

FinTech, Banking

In a rapidly changing world, more use cases in the Banking, Financial Services, and Insurance (BFSI) sector have resulted in an explosion of data to be analyzed and acted on. The segment has primarily focused on integrating data science throughout all decision-making processes based on actionable insights from customer data.

Digital Marketing

Customers’ websites, social media posts, web traffic display networks, videos, web pages, CRMs, databases, and so on are now fetching huge amounts of data. The analysis of such high volumes of information necessitates a high level of business intelligence that can only be obtained by employing data science methodologies correctly.

Given these reasons, digital marketing is increasingly dependent on data science for analytical purposes. This data-driven knowledge may assist marketing/brand managers in obtaining critical information such as understanding how returning customers interact on the website, understanding customer sentiment by analyzing data from social media interactions and surveys, identifying potential high-value prospects, and so on.

Healthcare

The data generated in the healthcare industry is voluminous and complex, making data science a critical field for improving patient outcomes and reducing costs. Data science has made it much easier to handle all of the information, from medical records, clinical trials, and genetic data to claims processing, smartwatch data, care management databases, scientific papers, and other items. In recent years, data science has helped healthcare companies produce an increasing number of jobs.

Retail and E-commerce

Even the worldwide pandemic, store closures, and job losses couldn’t drive down data scientists’ demand for retail goods. The consumer-focused retail sector thrives on constant personalization and relevance, with one objective in mind: to understand the shopper’s behavior and patterns through data.

Data science has aided retailers in improving consumer understanding. Data scientists are in high demand among retail companies since they have a unique combination of data expertise, business acumen, technological know-how, instinct, and statistical knowledge.

Cyber Security

Data science in cybersecurity has revolutionized the way information is handled, making it more actionable and intelligent than previous methods. It enables data gathering from appropriate cybersecurity sources and analysis that complements data-driven patterns.

This marriage of data science and cybersecurity is a significant paradigm shift from traditional security solutions such as user authentication, access control, cryptography, and firewalls to systematic data management.

Telecommunications

Now that people regularly connect to communications networks through voice, text messages, social media, and other channels, telecom companies have access to enormous amounts of data.

Other sources of data, such as website visits and past purchases, search patterns, and customer demographics like address, age, gender, and location have shown to be important for the telecom sector; this is where data science comes in.

Media And Entertainment

The big players in the media and entertainment business, such as YouTube, Netflix, Hotstar, and others have started using data science to get to know their consumers and offer them the most relevant and customized recommendations. Even basic entertainment networks and gossip newsfeeds are turning to user data for assistance.

The notion of addressability, as well as the capacity to interact with customers based on their choices, is a new face of digital reality that aims towards matching the users’ personal preferences. Along with the power to engage with consumers as per their wants, this concept of addressability evokes feelings of attachment and connection.

The media and entertainment sector is searching for data scientists who may collect, evaluate, store, and provide recommendations in order to have a positive influence on the company in such a world where everything is determined by data.

What Qualities Should A Data Scientist Have?

A data scientist is someone who can make sense of the information they are given. As a result, it’s critical that the applicants have strong analytical skills. Here are qualities every data scientist must-have from the get-go:

Critical Thinking

An analytical mind is a data scientist’s most critical quality. If you’re not able to think critically, data science may not be for you. As data scientists spend most of their time thinking about data, it will help if they have an extremely analytical mindset. Critical thinkers can analyze information from multiple perspectives and make informed decisions based on the data available to them.

Communication Skills are Essential

In addition to a passion for analytics and statistics, data scientists must have excellent communication skills in order to clearly convey their findings to management and other stakeholders who do not necessarily have technical backgrounds or a deep understanding of analytics principles. Data scientists should also be able to clearly communicate data visualization and data science results in a way that is easy for all stakeholders in the organization to understand.

Programming Skills

A good data scientist knows how to code and has the ability to use data science tools such as Python, R, or SQL. A data scientist must also be familiar with data mining toolkits such as Weka, SAS enterprise miner, or Orange.

Logical Thinking Skills

A data scientist must have logical thinking skills in order to see through a problem and spot any inconsistencies that might exist within the data set. They need to find out what is driving their business decisions by analyzing data on competitors, customers, products and services, etc.

Business Acumen

Data scientists should know how businesses work so they can understand where information comes from (e.g., accounting software) and how it’s used (e.g., financial statements). Data scientists will also need to understand data’s impact on the company’s strategic objectives.

Mathematical Skills

A data scientist must be proficient in basic mathematical skills, including but not limited to algebra, geometry, and calculus. They should also have some knowledge of statistics and matrix operations.

Domain Knowledge

It is extremely beneficial for data scientists to have domain knowledge in a particular area so they can better understand the questions being asked about that data set. This could include expertise in marketing, health care, finance, or any number of other fields.

Teamwork Skills

Data science is often a team sport, with different members bringing their own expertise to bear on the problem at hand. A data scientist should be able to communicate effectively with data engineers and data analysts to build data pipelines, as well as other members of the team who will use their results in one way or another. Communication skills are also important for data scientists to have because they’ll often be working with people from non-technical fields and need to explain complex ideas simply.

Flexibility

Data science is a fast-moving field where new trends emerge all the time. A data scientist should stay up on what’s happening in the data science world, keep learning about new techniques, and try out different tools when necessary.

It can even be valuable for data scientists to learn multiple programming languages so that they can work more efficiently depending on the situation at hand. For example, data scientists who can use Python and R will be able to work with data from a variety of different sources.

Curiosity

Data scientists need to be curious about the data they’re working with, both in terms of how it was collected and what insights it might contain. Good data scientists are always asking questions and looking for new ways to explore data. They think critically about their data, not just assume that everything is correct because that’s the way it has always been reported.

Maybe there were errors made during collection, or maybe there are better methods out there for collecting certain kinds of data? A good data scientist will try to find out more by digging deeper into their datasets rather than settling on first impressions.

Data Architecture

Understanding data architecture is critical for a data scientist to be successful. This implies being able to comprehend data models and how data is stored, as well as the various layers of data (source data, transformation data, target data). A data scientist must be able to understand data sources and data flows, as well as the data pipeline from soup-to-nuts. These concepts are critical for a data scientist in order to produce relevant insights into business questions.

How To Hire A Data Scientist?

There are many ways you can recruit and hire a top data scientist. But data scientists are much more complicated to recruit than traditional IT roles, they’re highly data literate and make decisions based on data-driven insights. And while data science is a hot discipline right now, there aren’t enough people with the required skill set to fill the available positions – so if you want to hire a data scientist who meets your requirements, you need to act fast.

Here are some tips for hiring your next data scientist:

1. Join online tech community groups and data science-focused meetups to find potential candidates. We have gathered 21 best developer communities to join and recruit – here.
2. Attend local tech meet-ups, data science groups, and conferences to find data scientists.
3. Consider using a recruiter or staffing firm that can source data scientist talent for your company.
4. Attend tech conferences and job fairs to find data scientists.
5. Use social media recruiting platforms (e.g., LinkedIn, Twitter) to find data scientists. Here’s our favorite social media platform for recruitment – here.

There’s no one way to find data scientists, but hopefully, these tips will get you started.

And, when you are ready to interview a data scientist, here’s our list of the best 70 interview questions and answers.

Final Thoughts

The data scientist role is an important one for any company. By understanding the qualities of a data scientist and what to look for when hiring, you can ensure that your data science team is productive and successful.

If you need help finding data scientist candidates, data science recruiters are available to help you find the right data scientists.

Our recruiter picks for recruiting this role:

Hirefuel is a premier boutique recruiting firm specializing in placing top talent with hyper-growth companies. Their proven track record is a testament to their passion and expertise in recruiting. As an extension of your brand, Hirefuel takes great pride in its relationships, ability to bring a personal touch, unrivaled experience, customization, and “startup speed” to talent acquisition.