How to Hire Data Scientist for Your Organization

Nowadays, data scientists are in high demand in various industries, ranging from healthcare to science, automotive, and research. In this Competitive era, every organization gathers the data To produce accurate reports for effective business strategies.

Some big organizations have an entire team of data scientists that collects structured and unstructured data to perform accurate analysis and make possible assumptions. However, not many can afford full-time data scientists. These types of small companies usually hire freelance data scientists on a project basis.

Glassdoor surveyed that Data Scientists are the best Jobs in America throughout the previous 3 years. According to a market research report, in the USA over 171,575 data science skills are in shortage. Presently, SME companies and startups have started realizing their need which led to the demand for data scientists, both full-time and freelance, and is simply expected to increase throughout the next few years.

Who is a Data Scientist?

A Data Scientist is just somebody who assists you with figuring out data or produces value from data. They help to analyze enormous and complex measures of data and hidden pattern examples, make significant derivations and assist with driving choices. Data scientists could help with building a predictive model for the deals of an item, by identifying the patterns of existing data.

Data scientists could work with machine learning algorithms and develop AI-based devices for automation or perform statistical analysis to perform laboratory research.

Cost to Hire a Data Scientist?

In Optymizefreelance data scientists commonly charge between $29 and $200 each hour, and the project fix rate is $2,000. The cost fluctuates per project—something like breaking down a dataset could be less than a forecasting model for a business.

When hiring a data scientist in any organization, it is important to know the detailed job description.

  • Final Goal: The issue you’re attempting to tackle, the difficulties you’re facing, and what outcome you anticipate.
  • The Deliverable: The last result or the outcomes that you’re hoping to get.
  • The Period: The number of days or weeks you anticipate the project should take.

Where to Find Data Scientists

To find the ideal data scientist for your company, think about experimenting with the following recruiting techniques:

  • Keep in touch with the colleges and universities in the area. You might be able to track down leads on promising recent data science graduates.
  • Search online for established data scientists. Data scientists may be self-promoting online. Make contact with these candidates to determine how interested they are in the position and to schedule interviews.
  • Professional Network. Request information about data scientists that other local businesses have employed in the past. Make contact with these candidates to arrange meetings.
  • Post your job online. Consider posting your job on recruiting sites to find and draw qualified data scientist candidates.

Choosing Between a Freelance Data Scientist and a Full-Time Position

Data scientists can be hired for full-time or freelance positions. For a brief period, a business might think about hiring a freelance data scientist to review its current data and make one-off conclusions. The company then makes better business decisions and increases profits using the data.

However, some companies might employ a data scientist full-time to keep an eye on their data. The full-time scientist gives the business regular reports on the data so they can keep using it to advance.

Types of Data Scientists?

Several different categories of data scientists concentrate on various facets of the business. These include:

1. Quality Analyst:

Usually, this position is found in the manufacturing sector. They use specialized equipment to gauge the effectiveness of assembly lines. Quality analysts increase productivity while upholding product standards and performance benchmarks.

2. Business Analytic Practitioners:

These assess a company’s operations, data, and personnel to boost investment returns.

3. Actuarial Scientists:

Actuarial scientists typically work for financial organizations like banks and insurance firms. They forecast future investment profits and losses using mathematical algorithms.

4. Software Programming Analysts:

Software programmers strive to make the programs used by businesses more efficient.

5. Spatial Data Scientists:

These data scientists use spatial data to forecast where and why particular events will occur.

Qualifications of a Data Scientist

  • Bachelor’s degree in computer science or a related field is required. A master’s degree in mathematics, computer science, statistics, or a related quantitative field is preferred.
  • Excellent knowledge of machine learning techniques and algorithms for classification, clustering, and prediction, such as Neural Networks, Naive Bayes, SVM, Decision Forests, etc.
  • common data science toolkits, including R, MatLab, Python Data Science Libraries, etc. Python excellency and the ability to improve upon existing algorithms.
  • To ensure that business goals are achieved, algorithms are developed, tested on actual data sets, and fine-tuned.
  • To solve particular business problems, the ML algorithms must be implemented in the production instance and integrated with the necessary data sources. extending to incorporate unique algorithms.
  • Scala, Hive, Spark, and other big data technologies are examples. Data visualization tools like Tableau and query languages like SQL and Hive.
  • A strong background in applied statistics, including distributions, statistical testing, regression, etc.

Roles And Responsibilities of a Data Scientist

You have specific goals for a data science project, whether you run a startup or are the IT leader of an organization. To handle particular tasks, you need a data scientist. These are listed below:

  • Collaborating with business stakeholders to comprehend the needs of the business;
  • Determining the technical specifications of a data science project in collaboration with business analysts, statisticians, and MIS reporting executives;
  • Deciding on a data science software development project’s structure and strategy;
  • Creating metrics and a measurement process to assess the project’s success;
  • Taking the initiative in data analysis and data visualization;
  • For a successful data science system, selecting and utilizing the best machine learning models; Enhancing the data pipeline’s quality;
  • Putting in place efficient tools for data analytics and, where necessary, creating programs for this; Being in charge of data science program optimization;
  • Communicating with all pertinent parties;
  • Working together with your larger team, which includes DevOps engineers, business analysts, project managers,testers, and infrastructure architects;
  • Directing a team of data scientists;Managing expectations of stakeholders and reporting project status.

How We Can Hire Data Scientists

Write the 5th step procedure to hire a data scientist.

1. Write a Catchy Job Description

The first step you should do is to prepare a clear and well-formatted job description. It must include all the necessary details of the role and its responsibilities, required level of experience, tech stack, and other interpersonal skills.

Avoid any marketing lines to hook qualified data scientists, such as mentioning false salaries.

2. Post them on Different Job Boards

Now that you have a concise and catchy job description, begin posting it on recruitment sites such as LinkedIn, Indeed, and other freelance marketplaces such as Optymize.

3. Collect Resumes and Cover Letters and Sort through the Resumes

Once you receive the cover letters and resumes from candidates, screen through their applications.

Find out desirable candidates by looking at their tech stack and other technical skills, such as in the case of data scientist proficiency in developing analytical solutions with Python, R, SQL, and Java.

Make a list and shortlist candidates based on your project requirements.

4. Conduct Interview

Inform candidates regarding their selection for the interview round and notify them of the time, date, and venue for the interview.

Conduct interviews and use technical questionnaires to test and assess candidates’ grasp of the subject, evaluate their performance, choose suitable candidates and make an offer.

5. Onboard

As soon as the candidate accepts the offer, onboard him to proceed with your project development. Make sure he gets a brief introduction to your company’s policies, rules, and culture to avoid any misinterpretation.

Benefits of Hiring a Freelance Data Scientist

As we know, the salaries of data scientists are increasing rapidly. Most companies struggle to access and afford expert data scientists. This is where independent freelance data scientists come in handy. Because remote data scientists cut down the cost of in-house resources and provide their availability irrespective of their client’s location, they are gaining popularity. Optymize has pre-vetted quality data scientists on board, with various engagement models that offer working in any time zone without a language barrier.


A MBA by education, Keshab Singha working as a Sr. Digital Marketing Analyst. He brings a counter-intuitive thought to the discussion adding a customer point of view which has become central to success in today’s digitally driven business.

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