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Hire Data Science Developers

Data Science Developers

Hire data science developer

Data science is an upcoming multidisciplinary field that uses mathematical algorithms and scientific inference to extract meaningful information and insights for an exceptionally large amount of unstructured and structured data. In data science, the algorithms are implemented through computer programs that are being run on immensely powerful hardware systems, and they also require a significant amount of data processing. This field combines mathematics, machine learning, visualization, computer sciences, domain knowledge and data analysis. Apart from this, data science involves various forms of data such as text data, video data, image data, time-dependent data etc. A data scientist is an individual with the skills to handle a very high amount of data while developing a model. In this process, he can extract a particularly good amount of meaningful information and insights using statistical and machine learning algorithms and concepts involving computer science.

So, a data scientist or a data science developer has to develop programming codes just like a software developer and combine them with statistical analysis to draw meaningful insights on business data and much more. A data science developer will be training a machine and developing skills in it. He is expected to have good background knowledge of mathematics, advanced analytics technologies such as predictive modelling and machine learning. A data scientist must create a hypothesis, infer it and use it in understanding the market trends. After performing the analysis and drawing conclusions, he must deliver it effectively using proper delivery tools and settings.

Learn more about data science

Originally data science would only deal with the scientific data where the data is interpreted to draw conclusions involving the scientific discovery. But that was in the past. Currently, data science has a lot more applications than that. A few new additions to the data science field are mentioned below: -

  • Artificial intelligence: The core element of data science is machine learning. But very recently, a new addition to data science has been made, and that is deep learning.
  • Smart applications/ intelligent systems: Increasing easy access and portability of data-driven intelligent applications have contributed to the data science field. This is because a major chunk of data science is built around machine learning like intelligent systems and smart apps.
  • Edge Computing: This concept is very recently developed and is highly related to IoT (the internet of things). The work done by Edge Computing is bridging the gap between processes involving data science and the source of information.
  • Security: This is the most important issue and a big challenge to achieve in any digital space. The few recent technologies involving data science have proven to be effective against malware and hackers. For example, techniques involving Machine learning are proven to be more effective than traditional algorithms involved in solving the issues related to system security.

Importance of data science

Data science now has its applications in many industries. A variety of problems can be solved with data science. Some companies are partially dependent on data science, while some are completely dependent on it. Machine learning techniques and data science can solve problems that couldn't be solved in any other way. Some exceedingly popular applications of data science are: -

  • In Google, complex algorithms involving Machine learning give people the most relevant search results on the internet. These algorithms rank the pages shown. This aids in providing the most useful results when users surf the internet.
  • Another example is Spotify, a popular music streaming application that provides users with music per their taste. Here they use data science algorithms to understand a user's taste in music and provide services accordingly. The advantage of this is that the company can draw the attention of many users looking for a platform that understands their needs.
  • Google assistant is also a result of data science and machine learning technologies. It can recognize an individual's speech and use it as a command to provide the most relevant search results.
  • Another bleeding-edge technology is the Autonomous driving vehicle. Companies like Waymo use LIDARs and very resolution cameras to capture 3d maps of the roads and live videos. These captured videos and pictures are then used to navigate through the riad using machine learning algorithms.
  • Another particularly important use of this technology is Facebook and many other social media platforms to filter abusive content and hate speech. Data science is used here to filter abusive content and hate speech under the age restriction category.
  • Boston dynamics is another extremely popular use of machine learning, a particularly important part of data science technology. They use data science algorithms in robotics to induce humanoid movements and actions in them.
  • Piracy detection (example in YouTube) is also a result of data science. Many videos are shared on this platform daily. Many of these are pirated copies of original content. Data science algorithms automatically remove these pirated copies from the platform.

Roles and responsibilities of data science developers

  • Analyzing large amounts of raw information is the basic job of a data science developer. 
  • The developer needs to have high analytical thinking, good statistical and analytical background. 
  • The developer should be able to create proper hypotheses, set goals and achieve them in time.
  • The data science developer must find out important and reliable data sources and compile them systematically.
  • The developer should process a large amount of unstructured and structured data and draw useful conclusions.
  • He should have exceptionally good analytical skills to analyze a good amount of information and discover new trends and patterns in it.
  • Other tasks include building predictive models, visually presenting the data, creating strategies and solutions to various business challenges. Finally, he should be able to work collaboratively with other technicians in a team.

Skills Required

  • Some experience is required to work as a data scientist/developer. This experience can be gained through several internships available.
  • The developer should also have some knowledge of data mining.
  • The developer should understand the concepts of operation research and machine learning.
  • Some knowledge of programming languages such as C++, Python, Java, R and SQL is a cherry on top.
  • Analytical mindset, mathematical skills, communication, and presentation skills are all required to be a good data science developer.

Learn more

Skuad can help you hire certified and experienced Data Science Developers from all over the world based on your hiring requirements, be it freelance, full-time, or contract.

Salary Structure for data science developers

Data science developers' salaries vary according to the place and level of employment. An average data scientist/data science developer in India can vary between US $ 13,000 to US $17,000 in many companies in India. While in the USA the pay is quite high. The pay range varies between US $95,000 to US $200,000. The entry-level developer may be around US $95k. The mid-level data science developer may earn around US $130k, while the experienced developer can earn around US $165k to US $250k across companies in the USA.

Data Science Certification

Certification in data science can be of great use as there are many vacancies available, and there is a shortage of good data science developers and technicians. Data science is used in almost all industries. Entertainment companies like Spotify, YouTube and many other brands use data science algorithms to track user preferences, hide and restrict inappropriate content and deliver specific content to specific users. Google uses machine learning techniques to filter searches and rank pages using the machine learning algorithm. Apart from this, banks use data science tools to keep up with the competition, avoid possible frauds and provide the most accurate services to their customers. The finance sector is also a top user of data science services. So, the scope is vast, and there are a plethora of opportunities for budding data science developers.

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Industry Expertise

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