As technology continues to advance every day, the world steadily collects more and more data. The more data we collect, the harder it becomes to collect, manage, and derive insights from the data. Every year the volume of data collected from a number of sources like day-to-day business transactions, Enterprise Resource Planning (ERP) Systems, Sales information, marketing assets, and more.
How To Hire A Data Scientist
The State of Big Data
The variety of data sources on which companies and businesses collect are also rapidly expanding. More and more types of data and data formats are being collected, from the traditional structured numerical data to newer types of unstructured data like documents, audio files, images, and even videos. The speed at which the data is collected and processed has quickly become real-time thanks to the widespread growth of the Internet of Things (IoT).
Data Science and the Role of Data Scientists
Data Science or Data-Driven Science according to Oracle is that “Data Science combines multiple fields including statistics, scientific methods, and data analysis to extract value from data.”
IBM also describes it as “Data science combines the scientific method, math, and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.”
Based on both Oracle and IBM, Data science involves preparing the data for analysis and processing, advanced data analysis, and reporting to help stakeholders and the company’s leadership team interpret the data and create solutions for the business.
Data science generally has a 5 step lifecycle or pipeline. This starts with data capture where raw structured and unstructured data is collected from multiple data sources. The next stage is preparing the data by cleansing, transforming, or formatting the data. The next stage is data processing where data scientists examine and process the data. The next stage leads to data analysis where the data is analyzed. The next stage is data communication where reports and data visualization are generated to help with data interpretation.
What Are Data Scientists
Data scientists help businesses gather, process, analyze, report, and interpret multiple types of data and large volumes of data. Their key role is to efficiently handle and interpret the data and reduce the risk of data loss or data corruption.
Data scientists use their technical, analytical, and social science skills to find useful trends and meaningful information to help drive business decisions.
What are the Skills Needed to Look for in a Data Scientist
Hiring a Data scientist is challenging because Data science tackles a lot of facets of a business organization like information technology (IT), business intelligence, marketing, software engineering, and more. This means that a Data Scientist needs to be adept at a wide range of skill sets.
General Skills Required
- Software Engineering
- Statistical Analysis and Mathematics
- Data Mining
- Data Cleansing
- Data Pooling and warehousing
- Big Data Tools and Platform
- Data Reporting
- Research and Analysis
Optional Specialization Skills
Aside from having a strong IT, Analytical, mathematical, and programming skillset there are specific skill sets, programs, and platforms that a Data Scientist can specialize in to increase their value.
- Business Strategy
For a Data Scientist to interpret data properly he must be familiar with business strategy. Having a good understanding of business strategy. Having a firm understanding of how a business operates and the problems it faces will help data scientists create better solutions to help businesses overcome challenges.
- Data Visualization
Data Visualization is the actual presentation of data that is reported in a meaningful format that helps stakeholders gain insight and information from the data. Having a strong grasp in data visualization will help data scientists create an elaborate and contextual presentation of data in both numerical and graphical format to help decision-makers understand the data.
- Python Programming
Python is one of the top programming languages that data scientists should have a strong grasp on. Python is an adaptable programming language that can handle a wide range of applications in data science like data mining and more.
- SQL Databases
SQL stands for Structured Query Language which is a domain-specific computer language that will help data scientists in managing and querying data in a data management system.
Important Information About the Field in Data Science
Data Scientists are now in constant demand according to a report published by Northeastern University. The demand for data scientists is forecasted to continue to increase thanks to the widespread adaptation of Big Data in global enterprises. In the report “The Quant Crunch” that is co-published by IBM, Data Science job openings are projecting to grow 15% at the end of 2020.
Highlights of the Data Science Field Field
- Increased Job Openings for Data Scientist with a projected 15% increase
- Shortage of about 190,000 skilled data scientists according to Mckinsey Global Institute.
- Data Scientist has an average salary of $113,309 a year
- Projected Long-term Growth in the Industry
- Great career growth with high-paying positions
5 Steps for Recruiting and Hiring a Data Scientist
1. Define Their Role in Your Organization
Before hiring a data scientist, it is important to understand and define their role in the company and the department they are assigned to. Data science is a very wide umbrella and in order to attract the candidate, you must identify the problems that you want your data scientist to solve or provide use-case scenarios that they can potentially apply solutions for. Defining their role in your organization will help narrow down the scope and will provide insights into what your organization needs.
2. Create a long term road map
The next step is to think ahead and plan the long term function of the role. This will require you and the stakeholders of your business to evaluate your needs for the job role and your future requirements.
3. Create a Job Description
After you have identified the role of the data scientist in your organization, the next step is to create a job description that will help attract the right candidate for the position. Create a job description that highlights the number of experience and skillsets needed.
4. Setting the Right Interview
Once the job description is posted and the recruiters have filtered out the unqualified candidates and sent a list of qualified candidates, then the next step is the interview. A candidate should have a relevant portfolio that you can review that will help verify their experience and skill set.
The interview should also include a live demonstration of the candidate’s skill through a simulated data review, data management, and data reporting test. The live demo and test will help filter out inexperienced candidates and help narrow down your pool.
5. Create a shortlist
After you have interviewed the candidates the next step is to create a shortlist of the candidates that best match what your organization needs. Discuss with relevant stakeholders which candidate you should choose and send a job offer to.