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Data Scientist vs Data Engineer 2023 – A Step-by-Step Hiring Guide

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Data Scientist vs Data Engineer 2023 – A Step-by-Step Hiring Guide | 9NEXUS
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Data is a highly valuable resource in the digital economy, often referred to as the new oil. It remains largely untapped, holding immense potential. Our modern society generates an astonishing amount of data, estimated at around 2.5 quintillions every day. With such vast quantities of data being produced, global enterprises are recognizing the urgency of implementing data science and analytics strategies to enhance their business performance. However, even in this era, there persists a common debate within businesses regarding the distinctions between data scientists and data engineers.

In the past, there were concerns that the IT industry would encounter a significant shortage of talent in the field of data science by the end of 2018. It was predicted that keeping up with the increasing demand for skilled data scientists would become more challenging. Furthermore, another assumption suggested that automation would eliminate the need for data scientists by 2020. However, contrary to these presumptions, assumptions, and disruptions, we have observed that the demand for data scientists continues to rise. In light of this, let us delve into the question of which role is more suitable: data scientist or data engineer.

What is a Data Scientist?

A data scientist is mainly responsible for mining and interpreting raw data, understanding trends, formulating hypotheses, proving or disproving their theories through proper analysis. They are also required to build predictive models using statistical techniques such as regression, classification and clustering algorithms. Data scientists are expected to be proficient in statistics, machine learning and programming languages like Python and R.

What is a Data Engineer?

A data engineer is the link between raw data and its processing. A data engineer manages and optimises data infrastructure for data collection, management, and transformation. They ensure that data is accessible and secure. Data engineers have strong technical skills in database management, ETL (Extract-Transform-Load) processes, analytics platforms, SQL and NoSQL databases and IT infrastructure.

Major Difference Between Data Engineer And Data Scientist

Data scientists and data engineers play pivotal roles in leveraging and analysing data, with each role guiding distinct aspects of harnessing this valuable resource.

If you are considering investing in data analysis and establishing a data-driven culture within your organisation, it is crucial to comprehend the disparities between data scientists and data engineers in 2022. Understanding these disparities will enable you to make informed decisions when hiring either a data scientist or a data engineer, aligning their skills with your specific needs to effectively achieve your objectives.

The primary distinction between a data scientist and a data engineer lies in their respective focuses. Data engineers concentrate on constructing and maintaining frameworks and structures that facilitate the retrieval and storage of data in an organised manner. Conversely, data scientists are primarily dedicated to analysing data, identifying trends, and extracting valuable insights that can assist organisations in making informed decisions to enhance profitability and productivity.

Data Scientist Roles and Responsibilities

Data scientists are expected to be proficient in programming languages such as R and Python, have solid expertise in mathematics and statistics, and should be well-versed in machine learning algorithms. A data scientist’s primary responsibilities include:

  • Collecting, merging, and analysing complex data sets.
  • Building predictive models using statistical techniques such as regression, classification, and clustering.
  • Developing new algorithms and tools to better understand data patterns and trends.
  • Generating insights that can help guide the development of new products or services, as well as inform strategic decisions within an organisation.
  • Present the analytical findings to the business leaders using various data visualisation tools.
  • Update the solutions or analytics process based on the feedback received.

Data Engineer Roles and Responsibilities

A data engineer is responsible for setting up the infrastructural framework required for data management and analytics. To accomplish this, they must possess an extensive knowledge of database management, ETL processes, analytics platforms, NoSQL/SQL databases, and IT infrastructure. Data engineers are tasked with:

  • Creating efficient database systems to store large data sets
  • Developing ETL processes for data extraction, transformation and loading into storage systems
  • Implementing ETL pipelines for data extraction from various sources such as APIs, web scraping, and databases
  • Developing analytics platforms to facilitate the analysis of large amounts of data
  • Develop datasets to be used in data modelling, mining, and production.
  • Enhance collection of new data and refine existing data sources.
  • Optimising existing data infrastructure for better performance.

Data Scientists Vs Data Engineers Tools: Best In 2023

Data scientists and data engineers enjoy access to a plethora of tools that can facilitate their work. The most popular tools are Python, R programming language, SQL, Hadoop, Apache Spark, Tableau, and Power BI. However, as the demand for data science and analytics continues to surge in 2023, several new tools have already been introduced. Let us explore the newest tools in detail.

Python – Python is an open-source programming language, designed to simplify complex data analysis tasks. It has a vast number of libraries that allow users to manipulate and visualize data with just a few lines of code. Python is used by both data scientists and engineers for advanced analytics tasks such as machine learning, deep learning, predictive analytics, natural language processing (NLP), and computer vision.

R Programming Language – R is a programming language that is used for statistical computing and graphics. It has a wide range of libraries to perform various data analysis operations such as linear regression, nonlinear models, time series forecasting, K-means clustering, decision trees, and more. In addition to this, it also has several libraries for visualising data.

SQL – Structured Query Language (SQL) is the language of databases. It allows users to access, extract, and manipulate data stored in relational database management systems (RDBMS). Data engineers use SQL extensively to fetch the required datasets from different sources, while data scientists use it to clean and format their data before performing further analysis.

Hadoop – Hadoop is an open-source framework that enables distributed processing and storage of large amounts of data in a distributed environment. It consists of two main components – HDFS (Hadoop Distributed File System) and MapReduce. Data engineers use Hadoop to manage the collection, organisation, and storage of big data.

Apache Spark – Apache Spark is a distributed processing framework that can be used to analyse large volumes of data in a highly parallelized environment. It supports various programming languages such as Java, Python, and Scala. Data engineers use Spark for tasks ranging from real-time stream analytics to machine learning pipelines.

Tableau – Tableau is a business intelligence platform that allows users to create interactive data visualisations and dashboards. Data scientists use Tableau to share their insights with stakeholders, enabling them to make informed decisions more quickly.

Power BI – Power BI is a cloud-based data analytics platform that covers the entire analytics life-cycle from data preparation and visualisation to analysis and reporting. Data engineers use Power BI to streamline the entire analytics process.

Hire According to Your Needs – Outstaffing or Full-Time

Let’s start by exploring the advantages of full-time hiring before delving into why outstaffing can be more advantageous in certain situations:

  1. Commitment and Loyalty: Full-time employees typically exhibit a higher level of commitment and loyalty to the organisation. They are invested in the company’s long-term success and are more likely to align themselves with its values and objectives. This commitment can foster a stronger sense of ownership and dedication to their work.
  1. Cultural Alignment: Full-time employees have the opportunity to integrate more deeply into the organisation’s culture. They can develop a better understanding of the company’s values, processes, and dynamics, which can lead to improved teamwork, collaboration, and cultural cohesion.
  1. Continuity and Consistency: Hiring full-time employees ensures continuity and consistency in projects and processes. These employees become familiar with the organisation’s systems, workflows, and requirements over time, which can lead to increased efficiency and smoother operations.
  1. Long-Term Skill Development: Full-time employment provides a platform for continuous skill development and growth. Employees can engage in professional development opportunities, training programs, and mentorship, enhancing their expertise and contributing to their long-term career progression within the organisation.
  1. Deeper Organisational Knowledge: Full-time employees have the opportunity to accumulate in-depth knowledge about the company’s operations, industry, clients, and internal dynamics. This knowledge can be leveraged to make informed decisions, develop strategic insights, and provide valuable context when solving complex problems.

Now, let’s explore why outstaffing (or staff augmentation) can be more advantageous in certain situations:

  1. Access to Specialised Skills: Outstaffing allows you to tap into a diverse talent pool with specialised skills that may not be readily available in your local area. You can find professionals with specific expertise and experience in niche areas, enabling you to address unique project requirements or overcome skill gaps within your existing team.
  1. Flexibility and Scalability: Outstaffing provides flexibility in scaling your team based on project needs. You can quickly ramp up or down the number of outsourced professionals to match the workload, ensuring optimal resource allocation and cost efficiency. This flexibility is particularly valuable for short-term projects, seasonal demands, or when you need additional expertise for specific phases of a project.
  1. Cost Efficiency: Engaging outsourced professionals through an outstaffing model can often be a cost-effective solution. You can avoid the expenses associated with full-time employees, such as salaries, benefits, office space, and training. Outstaffing providers typically handle these costs, allowing you to allocate your budget more efficiently.
  1. Rapid Onboarding and Time Savings: Outstaffing providers streamline the recruitment and onboarding process, saving you time and effort. They handle candidate selection, vetting, and administrative tasks, ensuring a faster turnaround time for project initiation. This agility enables you to meet tight deadlines and quickly adapt to changing project requirements.
  1. Access to Global Talent: Outstaffing opens up opportunities to work with professionals from around the world. This global talent pool brings diverse perspectives, innovative ideas, and a wealth of cross-cultural knowledge. Collaborating with outsourced professionals can broaden your organisation’s horizons and enhance your problem-solving capabilities.
  1. Risk Mitigation: Outstaffing can mitigate the risks associated with hiring full-time employees. If a project concludes or there is a shift in requirements, you can easily disengage from the outsourced professionals without the legal and financial complexities of terminating full-time contracts.

Efficient Data Scientist and Data Engineer Outsourcing Solutions with 9NEXUS

At 9NEXUS, we specialise in providing efficient outsourcing solutions for data scientists and data engineers. With our comprehensive services, we enable organisations to harness the power of data without the challenges of maintaining an in-house team

Our team at 9NEXUS consists of highly skilled and experienced professionals in the field of data science and engineering. We carefully assess your project requirements and match you with the right experts who possess the specific skill sets needed to drive your data initiatives forward. Whether you need data scientists to extract valuable insights from complex datasets or data engineers to design robust infrastructures, we have the talent to meet your outsourcing needs. 

By partnering with 9NEXUS for data scientist and data engineer outsourcing, you can enjoy numerous benefits. We offer flexible engagement models, allowing you to scale your resources based on project demands. Moreover, our cost-effective solutions help optimise your budget, as you avoid the overhead costs associated with hiring full-time employees. With 9NEXUS as your outsourcing partner, you can focus on leveraging the power of data to drive innovation and achieve your business goals.

Conclusion

Choosing between full-time hiring and outstaffing depends on your specific needs and requirements. While full-time employees can provide longer-term continuity, outsourced professionals offer flexibility, cost efficiency, and access to specialised skills. Consider the advantages of each approach before deciding which model is best suited for your organisation. To ensure success in either case, it is important to invest in a comprehensive recruitment process and onboarding strategy. That way, you can ensure that the right people are brought on board for your next project. By leveraging the advantages of both full-time hiring and outstaffing, you can maximise the potential of your organisation and elevate its performance to new heights.

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Outstaffing Solutions | 9NEXUS
Outstaffing Solutions | 9NEXUS
Outstaffing Solutions | 9NEXUS

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