Whether you want to create a modern website powered by the machine learning capabilities or want to add live chatbots to it, you need the help of a data scientist. The data-driven automated decisions play a critical role in modern businesses. You need to hire data scientists for implementing machine learning and artificial intelligence capabilities to your software development. A data scientist is highly necessary for any web or application development that has capabilities to process real-time information and the behavior of the online visitors.
The demand for the data scientist degree holders and specialists is continuously increasing in the global marketplace due to the pervasive use of data visualization, analysis, and the implementation of data-driven decisions in business applications.
What Is a Data Scientist?
A data scientist is a person with excellent skills in mathematics, computer science, and statistics who can deal with the complex business, market, environment, and customer behavior data to skim useful information. That information is used for making data-driven decisions for the business policies as well as for the software tool development.
Numerous web applications like eCommerce chatbots use machine learning and data analysis to provide human-like services based on data processing and behavior learning. According to the IBM research, more than 85% of the businesses worldwide will use robot agents for handling customer interactions by 2020. That means a big increase in the demand for the data scientist is around the corner. Owing to this increasing demand, the average of an entry-level data scientist’s salary is about $109,727 per annum in the USA market.
Why Hire Data Scientists?
The global market is facing severe data scientist crunch in all major markets of the world. According to the latest survey, 82% of the global software development teams are planning to hire data scientists and data analysts and over 70% of teams are planning to expand their teams by the mid of 2019. There are many great reasons for hiring at least one or more dedicated data scientists for your company, a few of them are mentioned below.
#1 Data-Driven Decisions
Making intuition-based decisions cannot be so effective and successful as compared to data-driven decisions. The business environment has become so competitive that you cannot afford any mistakes in your business decisions. You need to be perfect in your decisions, business processes, and supportive tools.
#2 Full Utilization of Your Strengths
Finding out the full strengths and potential of your business, available resources, and possible business positioning will help you utilize your strengths more effectively and efficiently. Professionals with excellent data scientist skills can help you utilize your strengths comprehensively.
#3 Finding Out Right Trends
Data scientists help you find out the right trends in any particular field of business. By using the services of a skillful data scientist, you can grab better business opportunities by proper positioning following the emerging trends.
#4 Creating a Competitive Edge
An excellent data scientist can create a desirable competitive-edge over the competitors by proper utilization of the resources, implementation of process automation, and proper positioning to the future trends. Without having a data scientist in your team you would be able to compete with the stronger business competitors.
#5 Right Use of Technologies
All modern businesses are knowledge-based ones and need to use the power of modern technologies. The use of the right technologies for the right purpose is possible with the help of data scientists. Proper automation of the business processes with the help of data professionals can make your business flourish significantly.
12 Ultimate Questions to Hire the Best Data Scientist
12 ultimate data scientist interview questions with hints about the answers are listed below.
Q1: What was the reason to choose the career path of a data scientist?
Answer: The answer should describe the keen interest, positive attitude, and curiosity to do something new.
Q2: What are the major competencies, which you are an expert at?
Answer: The answer should include data collecting, processing, analyzing, and data visualization. The business acumen and the desire to explore value from the unstructured data should also be a part of the answer.
Q3: What programming languages do you have command at?
Answer: The answer should include at least a few major general-purpose languages such as Python, Java, R, C, C++, and SQL.
Q4: What major data platforms have you worked with?
Answer: A few major platforms like Hadoop, Hive, Tableau, Cloudera, SAP HANA, and others.
Q5: What are the major types of data sampling?
Answer: The answer should include cluster sampling, multi-stage sampling, stratified sampling, systematic sampling, and simple random sampling.
Q5: What are false positive and false negative errors?
Answer: False-positive error is also referred to as type I error. When the null hypothesis is true but the hypothesis is rejected, then the false positive error occurs. On the other hand, when the null hypothesis is not true but the hypothesis is not rejected; it is called as false negative or type II error.
Q6: Name the major assumptions used in linear regression?
Answer: 1) Linear relationship between the dependent variables and the regressors exist 2) Very minimal multicollinearity exists among explanatory variables 3) Homoscedasticity exists for all predictor variables & 4) Residual data errors are distributed and independent
Q7: Explain the Binomial Probability Formula?
Answer: The complete details of the Binomial Probability formula should be explained, which talks about the probability of success numbers for the ‘N’ number of trials.
Q8: Name the major parts of the Hadoop data platform.
Answer: MapReduce, YARN, and HDFS
Q9: Show me a portfolio of your Python-based work and explain about it?
Answer: This answer should include a detailed software development project developed in the Python language. The candidate should explain the entire project.
Q10: What sorting algorithms are used in the R programming language?
Answer: The R programming uses bubble, selection, and insertion as the sorting algorithms.
Q11: What are a few examples of group functions in SQL database?
Answer: AVG, MIN, DISTINCT, COUNT, and SUM
Q12: Which rule is extensively used for the model validation purpose?
Answer: The 80/20 rule is extensively used for model validation in data modeling.
What Are Data Scientist Competencies?
The data scientist is a role that deals with the scientific domains of knowledge in the artistic way to find out the information of business value. To accomplish this, a data scientist should have a strong grip over the core competencies as mentioned below.
#1 Data Analysis
The data analysis is one of the most fundamental competencies that a data expert should have. He/she should have statistical analysis competencies like regression analysis, cluster analysis, and others. He/she should also be able to determine what analysis method should be used.
#2 Database Designing
Data designing is another important competency of a data scientist because he/she has to play with the data pushing, processing, and analyzing.
#3 Computer Programming
Computer programming is one of the three fundamental competencies of a data scientist. He/she should have strong programming skills of the most relevant programming languages commonly used for the data analysis.
#4 Data Visualization/Presentation
Visualization of the data into the most understandable formats and manifestations is one of the core competencies of a data scientist. This helps data scientists in communicating the information more effectively.
#5 Business Wisdom
Without business wisdom, there is no meaning of a data scientist. This is the most powerful competency that helps a data scientist to create business value for the company. He/she should have both knowledge and vision about the domain business he/she works in.
A strong desire to find out the solution to the complex problems through an innovative approach and with the use of modern tools and technology makes an excellent data scientist.
#7 Technical Education/Training
An excellent data scientist should have formal as well as informal expertise. Diplomas and data scientist certifications along with the formal university degrees are the fundamental data scientist requirements to make a great data scientist. Specialized training and hands-on experience bring higher salaries for the data scientists in the marketplace.
Data Scientist Must-Have Skills
The list of must-have skills of a data scientist is mentioned below.
- Python, Java, C, C++, Perl
- SQL database coding
- Apache Spark
- Hadoop platform
- Machine learning
- Unstructured data
- R language
- MS Excel
Modern businesses cannot survive without business intelligence and the effective use of technologies. Data scientists help enterprises to create competitive-edge, and business opportunities by using the power of data analysis. So, every business should hire at least a data scientist for his/her business so that the business thrives in a fiercely competitive marketplace.