How to Hire Data Scientists Right: Pitfalls to Avoid
Data science has entered modern business at a very quick pace and continues to find more and more adepts. Companies use and store huge volumes of data that can be useful if correctly deciphered. This is what data science and analytics is for. We have also discussed data scientist salary around the world in our blog.
Dataconomy defines data science as a broad term including many notions such as math, scientific method, statistics and many tools that are used to extract insight and knowledge from data sets. In other words, it is a sphere that uses a variety of tools in order to work with big data and get information from it that can be useful for business. Moreover, data science is very important for artificial intelligence and machine learning. Virtual entities are able to learn from the results that data science produces, followed by functions and algorithms. Meanwhile, IBM defines data science as an evolution from the business or data analytics. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. Logically, big data scientist looks at broad amounts of data and tries to make a connection, and sharpen it down, and further derive meaningful compilation. Of course, the role of the data scientist is of high demand among organizations who are desperately looking for experts who can do the data organizing and prepare it for analysis, as InfoWorld says. Data analytics is a more concentrated form of data science, according to Dataconomy. It is a more focused version of data science, where data is scanned and parsed out with a specific aim.
Modern Trends and Tools of Data Science
Active Wizards defines 6 trends of data science development in 2017:
- The number of businesses moving their prediction analytics demands to the cloud is extremely rising.
Many companies move their data and apps to the cloud, because it is flexible and reduces the complexity of configuration and administration of resources. That is why key cloud providers offer their own Machine Learning services.
- More companies adopt Spark and Hadoop big data platforms.
Big Data technologies are steadily growing, for example, Spark and Hadoop. The latter is able to collect broad amounts of data with its further distribution to low-cost servers that run in parallel. Apache Spark uses in-memory computation and is one of the rapidly growing big data platforms.
- Strong data security.
Cyber attacks are increasing, so to protect data from them, data security is strongly needed. Machine learning algorithm is used to detect anomalies, AI conversational interfaces (bots in other words) are utilized for security responses to emerging threats and automation of assistance. Behavioral biometrics is also one of the rapidly developing areas nowadays. Combined with Machine learning, it helps to improve efficiency and reduce the costs. It helps to identify people who are carrying out the attack and distinguish them from robots.
- Deep Learning technology.
Deep learning becomes a mainstream nowadays, because it has shown great results for a lot of important apps, such as facial recognition, machine translation etc. AI will upgrade to AGI (artificial general intelligence).
- Chatbots and other conversational interfaces.
Conversational systems have developed to the extent where the computer “listens” to the user and further adjusts to his expected results. Chatbot technologies are integrated into many consumer applications and continues to move into other spheres like e-commerce, marketing campaigns, or enterprise solutions.
- Self-driving cars.
Autonomous technology is almost ready to hit enter the market, as car manufacturers tend to produce hands-free cars that might reduce the amount of accidents on the road. Google, Tesla, BMW, General Motors and other auto giants have already presented their new projects.
Knuggets name the best data science and analytics tools. According to the annual survey conducted by the website, R is the leading tool (49 per cent). It grew from 46.9% in 2015. Python usage displayed faster growth with 45.8% from 30.3% in 2015. RapidMiner has about 33% and remains the most popular general platform for data science. Other tools worth attention and growing popularity include Amazon Machine Learning, Dato, MLlib, Dataiku, H2O, IBM Watson and scikit-learn.
Job Outlook for Data Scientists and Big Data Analysts
Information provided by Edureka.
Skill Set of Data Scientists
Regarding data scientist skills, there is a number of preferred operations that a good data scientist should be able to perform. According to IBM, data scientists get solid foundation in statistics, computer science and applications, analytics, modeling and math. They usually work with Python, Square Root team, R and SQL. Data science tools and programming languages that are most frequently demanded by employers were listed by DataScienceWeekly:
IT data scientist takes the project from the very start to its finish using a variety of tools. He also should effectively communicate his findings.
Skill Set of Data Analysts
Big data analyst should be proficient in R., SQL, and Python. He should also be knowledgeable in statistics. Data analysis is like combing through data sets to find important information that can be useful for reaching company’s goals. Data analysts sorts big data into specific sets relevant to company’s request and can measure events in present, past, or future. Data analytics usually connects patterns and trends with the aims of organization, – InsideBigData informs. PWC states that business intelligence, visualization of data, data warehousing, optimization and operating systems, ETL, scripting languages, principles of software development, EPR systems, statistical software.
Pitfalls to Avoid When Hiring Data Science Experts
Entrepreneur defines 5 common mistakes during data expert hire, such as:
- Job title is imprecise
- Failure to emphasize interesting problems
- Experience is defined too narrowly
- Sourcing strategy is undifferentiated
- Skill validation process is inconsistent
Meanwhile, Knuggets sees 7 pitfalls that may occur when hiring data scientists or analysts:
- Confusing between causation and correlation
- The right visualization tools are no chosen
- The right model validation frequency is not chosen
- No question/plan during analysis
- Attention is paid to data only
- Probabilities are ignored
- A model is built on the wrong population
In order to avoid them, you should pay attention to the job experience, practical skills of the candidate. You should also check if the person has good communication skills and statistical knowledge. It is better to prepare a test assignment for the candidate to see whether he is the perfect worker for you.
Big Data Analyst CV
Data Scientist Resume
Python Data Developer Resume
Where to Hire Big Data Experts
Since data science becomes the core of the majority of giant organizations worldwide, job openings are rising in number. If your company deals with sufficient amounts of incoming data, you can make profit of it. The only thing you’ll need to do is to find an experienced professional who can take care of your big data and develop necessary algorithms and trends that would be helpful for your business development.
Hire data scientists at Mobilunity, our experts have sufficient experience in the international market due to cooperation with the key organizations in banking, e-commerce, medical, IT, commercial industries. Moreover, you will be pleasantly surprised with their competence and the costs.