How to Hire a Data Scientist

A data scientist is essential for any business looking to interpret data, which is fundamental to succeeding in today’s data-driven environment. A data scientist relies on a combination of statistical methods, machines, and analytical brain power. They are contributed by organizations that want to collect, cleanse and validate their data, often for artificial intelligence (AI) and machine learning (ML) projects. Data scientists help identify patterns that can then be leveraged to improve data-driven decisions, business processes, and strategies.

Why hire a data scientist?

When the right data scientist is engaged in your business, they can add value to your business in a variety of ways.

Some of the benefits of hiring a data scientist include:

  • Better decision making: An experienced data scientist can leverage the power of data to improve decision-making within your business.
  • Data monetization: By hiring a data scientist, you take a step towards monetizing your data, which is a major source of revenue for many large companies today.
  • In-depth understanding of customers: A data scientist can help your business monitor any changes in customer behavior, deepen your customer base, and improve your business model.
  • Unique information: Through effective data analysis, data scientists uncover unique insights that were previously inaccessible to human leadership alone.
  • Grow your business: Data scientists can help your business discover new markets that may be interested in your product or service. For example, they could examine advertising campaigns and determine the type of new customers acquired through a particular initiative.

These are just a few of the many benefits of hiring a data scientist.

Field competition

The role of a data scientist is highly sought after across industries due to the growing importance of data. There are countless organizations looking for the best data scientists, and the demand for them is only increasing. Just as a data scientist competes for a job, you compete with other organizations for the data scientist.

That’s why it’s so important to streamline the process of hiring a data scientist while making sure to keep your standards high. If you can’t streamline the process, chances are another company will step in.

Top data scientists have a diverse set of skills, not just data science skills. It is important for them to have time management skills since the role requires taking on multiple tasks simultaneously, as well as strong communication skills to help maneuver the business and technology areas.

The skills of a data scientist can be broken down into two broad categories: technical and soft skills.

Some of the most in-demand skills for technical data scientists include statistical analysis and computer science, machine learning, deep learning, data visualization, data manipulation, math, programming, statistics and big data.

As for soft skills, your data scientist should have strong communication skills, incredible data literacy and intuition, people management, critical thinking, flexibility, adaptability, and patience.

Types of Data Scientists

The title “data scientist” can actually mean different things since there are different types of data scientists. When looking to hire the best data scientist for your business, you want to make sure you know what aspects of the business you want them to address.

The different types of data scientists include:

  • Quality Analyst: Quality analysts typically work in the manufacturing industry. They rely on specific tools that help them measure assembly line efficiency and improve work speed while maintaining product quality.
  • Business Analytics Practitioners: These types of data scientists examine a company’s procedures, data, and employees to help improve returns on investment.
  • Software programming analysts: Software programming analysts improve business programs to reduce computing time.
  • Spatial Data Scientists: Using spatial data, these data scientists can predict where and why certain events occur while using data to find correlations between events.
  • Actuarial scientists: Often operating in financial institutions, actuarial scientists use mathematical algorithms to predict future profits and losses from investments.

Define clear roles and responsibilities

When looking to hire the best data scientist, one of the best things you can do is provide a clear job description with defined roles and responsibilities. This can include a list of potential data science use cases, required skills and technology stack, work summaries for day-to-day operations, and clearly laid out timelines.

It’s always best to include as much information and transparency as possible, which will make it more appealing to top talent. Accurate and specific job descriptions are often overlooked by companies despite the fact that they are extremely important.

At the same time, be sure not to overstate the skills and experience required, otherwise you risk making the pool of candidates too narrow. It’s best to focus on the skills and experiences that are essential to the business.

The interview process for a data scientist can often be unstructured as the role has only been established for a little over a decade. Since then, he has evolved into a wide variety of specialist roles like data engineer, machine learning engineer, research scientist, and more. This means it’s important to customize the interview process to specific company needs, and second-round interviews can be more focused on core skills such as programming, statistics, learning automatic, deep learning and mathematics.

Popular hiring networks like Toptal

Just as the role of a data scientist has evolved and transformed over the years, so too has the process of recruiting top talent. Many companies are turning to non-traditional methods of hiring, especially as the world embraces freelance and remote work. One of the most popular options for hiring data scientists and other high-level talent is Toptalwhich is an exclusive network of top independent talent.

The Toptal platform uses artificial intelligence to help companies find the best data scientist for their job, and the talents provided by the platform are in the top 3% of their respective fields.

Serving over 6,000 clients across different industries and providing talent to many of the world’s largest companies like AirBnB and JPMorgan Chase, Toptal ensures companies find the best data scientists. Platforms like this are crucial for businesses today, especially those looking for data scientists, as the field is so competitive.

By bringing in the best data scientist in your business, you’ll be able to leverage data to gain previously inaccessible insights while improving the efficiency of all operations.

Another option is to use AI, the Manatal platform simplifies the entire hiring process by suggesting the best data scientist candidates for a given position while automating redundant tasks.

Its AI recruiting software is designed to find and hire candidates faster. Designed for HR teams, recruitment agencies and headhunters, it’s simple to use yet powerful.

Simplicity means there’s no steep learning curve, it’s easy to customize a recruiting pipeline to suit your process with a sleek drag and drop interface. You can also easily view your recruitment progress in a single view.

Quickly scale your recruiting efforts, some of the features include:

  • Share your job openings on over 2,500 free and premium channels, including local, global and specialized job platforms such as Indeed, LinkedIn, Monster, CareerJet, JobStreet and many more.
  • Manage all your sponsored ads campaigns from a single platform.
  • Related Recommendations: Evaluate candidate profiles against job requirements to facilitate your selection process.
  • Enrichment of candidate profiles: Enrich candidate profiles with LinkedIn and other social media data for better recommendation matching.
  • Collect information beyond the CV. Manatal AI Engine crawls the web for data on more than 20 social networks and public platforms to automatically enrich candidate profiles.

Sean N. Ayres