A data scientist is crucial for any business looking to interpret data, which is fundamental to success in today’s data-driven environment. A data scientist relies on a combination of statistical methods, machinery, and analytical brain power. They are brought on by organizations wanting to gather, clean, and validate their data, oftentimes 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.
We feature 3 of the top ways to hire a data scientist.
Just as the role of a data scientist has evolved and transformed over the years, so did the hiring process for top talent. Many companies turn to non-traditional ways of hiring, especially as the world embraces freelance and remote work. One of the most popular options for hiring data scientists and other top talent is Toptal, which is an exclusive network of top freelance talent.
The Toptal platform uses artificial intelligence to help companies find the best data scientist for their job, and the talent provided by the platform is in the top 3% of their respective fields.
Serving more than 6,000 customers 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 today’s businesses, especially those looking for data scientists, as the field is highly competitive.
By bringing on the best data scientist to your company, you will be able to leverage data to achieve insights previously unattainable while also improving efficiency across operations.
Another great option for hiring a data scientist is Turing, which provides their AI-backed Intelligent Talent Cloud to help source, vet, match, and manage the best remote software developers across the globe. Used by some of the world’s top companies like Pepsi, Dell, and Coinbase, the platform leverages global sourcing, intelligent vetting, extensive matching, HR/payments compliance, and automated on-the-job quality control.
Turing does an excellent job of making the remote hiring process easy for both companies and developers. Companies can hire pre-vetted, highly-qualified remote software talent that spans across more than 100 skills. The process only takes 3-5 days.
The Intelligent Talent Cloud relies on AI to vet, match, and manage over 1.5 million developers around the world, saving companies a ton of time and resources as they construct an engineering team in days.
Here are some of the top features offered by Turing:
AI-backed Intelligent Talent Cloud
More than 1.5 million developers worldwide
Helps construct engineering team in days
Pre-vetted, highly-qualified talent
100+ skills among talent
Another option is to use AI, the Manatal platform simplifies the whole hiring process by suggesting the best data scientist candidates for a given job while automating redundant tasks.
It's AI Recruitment Software is designed to source and hire candidates faster. Tailored for HR teams, recruitment agencies, and headhunters it is simple to use yet powerful.
The simplicity means there is no steep learning curve, it is easy to customize a recruitment pipeline based on your process with a slick drag-and-drop interface. You can also easily overview your recruitment progress in one single-board view.
Scale your recruiting efforts quickly, some of the features include:
- Share your job openings on 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 job advertising campaigns from a single platform.
- Matching recommendations: Score candidates' profiles based on job requirements to facilitate your screening process.
- Candidates' profiles enrichment: Enrich candidates' profiles with LinkedIn and other social media data for better matching recommendations.
- Collect insights beyond resume. Manatal AI Engine browses the web in search of data on 20+ social media and public platforms to automatically enrich candidates' profiles.
Why Should You Hire a Data Scientist?
When the right data scientist is brought on to your company, 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.
- Monetizing data: By hiring a data scientist, you take a step toward monetizing your data, which is a major revenue source for many of today’s top companies.
- Deeper understanding of customers: A data scientist can help your company monitor any changes in customer behavior, provide a deeper look into your customer base, and improve your business model.
- Unique insights: With effective data analysis, data scientists uncover unique insights that were previously unattainable by human leadership alone.
- Expand your business: Data scientists can help your business uncover new markets that might be interested in your product or service. For example, they could review advertising campaigns and determine the type of new customers that were gained from a particular initiative.
These are just some of the many benefits to hiring a data scientist.
Competition in the Field
The role of a data scientist is highly sought after across industries due to the increasing importance of data. There are countless organizations searching for the best data scientists, and the demand for them is only increasing. Just like a data scientist is competing for a job, you are competing with other organizations for the data scientist.
This is why it is so important to streamline the process of hiring a data scientist while making sure to keep your standards high. If you fail to streamline the process, there is a strong chance that another company will swoop in.
The best 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 demands taking over multiple tasks simultaneously, as well as strong communication skills to help maneuver the areas of business and technology.
The skills of a data scientist can be broken down into two main categories: technical and non-technical skills.
Some of the most sought after technical data scientist skills include statistical analysis and computing, machine learning, deep learning, data visualization, data wrangling, mathematics, programming, statistics, and big data.
As for non-technical 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 of “Data Scientist” can actually mean a few different things given there are different types of data scientists. When looking to hire the best data scientist for your business, you want to make sure to be aware of which aspects of the company you want them to tackle.
The different types of data scientists include:
- Quality Analyst: Quality analysts usually work in the manufacturing industry. They rely on specific tools that help them measure the efficiency of assembly lines and improve the speed of work while maintaining product quality.
- Business Analytic Practitioners: These types of data scientists look at a business's procedures, data, and employees to help improve investment returns.
- 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 happen while also 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 tech stack, work summaries for day-to-day operations, and clearly established timelines.
It is always better to include as much information and transparency as possible, which will make it more attractive to top talent. Accurate and specific job descriptions are often overlooked by companies despite the fact they are incredibly important.
At the same time, make sure not to go overboard with the required skills and experience or else you risk making the applicant pool too narrow. It’s better to focus on the skills and experiences that are critical to the company.
The interview process of a data scientist can often be unstructured due to the role only being established for a little over a decade. Since then, it has evolved into a wide variety of specialized roles like data engineer, machine learning engineer, research scientist, and more. This means it is important to customize the interview process depending on the company’s specific needs, and second-round interviews can be focused more on core skills like programming, statistics, machine learning, deep learning, and mathematics.
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