The Evolution of Data Science Jobs: A Look at the Roles and Responsibilities in Today’s Market

UBC Master of Data Science Program has almost come to its halfway through, and as a future job seeker in Data Science, I got exposed to many questions about various job roles in this field. I started researching multiple job roles in Data Science and learning about how they differ regarding their required qualifications, responsibilities, and career development journeys. Throughout this research, I have gained valuable insights that I am sharing in this blog post.

Before jumping into talking about hard-core technical concepts and skills, let’s remind ourselves of the definition of data science provided by “Big Blue”, A.k.a IBM:

It’s important to understand the different types of tasks that are typically found on a data science team which are related to the core scientific disciplines like Math and Statistics, Programming, Analytics, AI/ML, Decision-making, and Strategic Planning. This will help to define the overall competencies and skills that are needed on a data science team, regardless of roles.

The chart below builds a solid foundation for us to understand the various roles in data science and their origination. In the next step, we will discuss the required skills and specific job titles, and add more details on top of the chart presented below.

The chart above shows the major scientific disciplines involved in this ever-evolving field. Computer Science, Math, and Statistics, along with Business used to be the dominant players in this field. However, concerns about the ethical use cases of data and the “Ethical impact of data science” have risen many questions in the past decade and gained the interest of data scientists and researchers.

After taking a deeper look at the scientific disciplines intersecting around data science, the major roles in this field can be gathered under found umbrellas:

We often see job seekers not knowing what type of job roles they should be applying for or companies struggling with the low efficiency of their organizational structure. Therefore, clarifying the potentially defined roles related to each discipline mentioned above not only helps the individuals interested in pursuing a career in data science to identify the specific roles and responsibilities that align with their skills and interests but also helps organizations to identify the specific roles and responsibilities that are needed to build an effective data science team.

The diagram above summarizes the required skills for Data Scientist, Data Engineer, and Business Analyst roles. However, it lacks the Data Ethics and Privacy related roles that will be discussed below.

Every massive cargo ship you see voyaging in the ocean has an engine room and for any data-intensive application, data engineers are those mechanics and engineers working in the engine room of the ship. Welcome to the engine room of data science where things get grea he he he sy (JK 😂 Referring to the fictional character of the Trailer Park Boys series, Bubble.). Meet Data Engineering, the heroes who design and build the infrastructure to store, process, and analyze the vast amounts of data that fuel the data science world. Without them, data scientists would be lost in a sea of unorganized information, but with their skills and expertise, they turn data into a valuable asset for organizations worldwide.

Based on Preciesely’s glossary, Data Engineering is defined as the process of designing, building, and maintaining the infrastructure and architecture that allows for the efficient storage, processing, and analysis of data. Data Engineers are responsible for creating and maintaining the systems and pipelines that collect, store, and process large amounts of data from various sources. They work closely with data scientists and analysts to ensure that data is accessible, reliable, and of high quality.

Data Engineers are skilled in:

Salary ranges for Data Engineers vary based on experience level. According to data from 441 salaries, from PayScale website, an entry-level Data Engineer with less than 1 year of experience can expect to earn an average total compensation of 78,382 USD. This includes tips, bonuses, and overtime pay. For those with 1–4 years of experience, the average total compensation increases to 89,728 USD, based on 3,705 salaries.

As experience increases, so does compensation. A mid-career Data Engineer with 5–9 years of experience can expect to earn an average total compensation of 108,703 USD, based on 1,401 salaries. For those with 10–19 years of experience, the average total compensation is 120,237 USD, based on 674 salaries. In their late career, with 20 years or more of experience, Data Engineers can expect to earn an average total compensation of 119,354 USD.

The Data Engineering field has experienced exponential growth in the past decade associated with the rise of cloud computing platforms such as AWS, GCP, and Azure, Big data technologies such as Hadoop and Spark, and NoSQL databases. Therefore it’s not surprising that in the survey the salary level hasn’t increased for the high experience.

Let’s get back to the cargo ship example and ask a question about those who navigate the vessel in the ocean, and drive it to its destination, the Data Scientists! They are the masterminds behind uncovering hidden insights, spotting patterns and trends, and turning data into actionable knowledge. With a mix of curiosity, creativity, and technical skills, Data Scientists are modern-day alchemists, turning data into gold for organizations worldwide. Let’s dive into the exciting world of data science and discover the role of a Data Scientist with a good old joke about this role!

Getting back to formality, Data Science is an interdisciplinary field that involves using scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data (Amazon Web Services). It encompasses a wide range of techniques and tools from statistics, machine learning, and computer science to extract insights and inform decision-making.

As a Data Scientist, one can expect to earn an average total compensation at the entry-level, with less than 1 year of experience, of around 86,296 USD based on 1,464 salaries. Those who are early in their career, with 1–4 years of experience, can expect to earn an average total compensation of around 97,299 USD based on 7,740 salaries. As they progress in their career, with 5–9 years of experience, a mid-career Data Scientist can expect to earn an average total compensation of approximately 112,422 USD based on 2,434 salaries. Those who are experienced, with 10–19 years of experience, can earn an average total compensation of about 125,127 USD based on 631 salaries. In their later career, Data Scientists with 20 years or more experience, can expect an average total compensation of around 136,914 USD. (Source: PayScale salary report)

Business Insight or ML/AI product management roles are in charge of transforming business problems into technical problems. In the cargo ship example, this is responsible for explaining the departure and destination locations to the Capitan and the vessel crew. A Business Insights and Analytics Manager is responsible for leading a team of data analysts and scientists in the development and implementation of business intelligence and analytics strategies. They are responsible for identifying and analyzing key business metrics and trends and communicating insights and recommendations to stakeholders to support decision-making and drive business growth. Business Insights and Analytics Managers need a combination of strong technical, analytical, and business skills to lead teams, develop and implement data analytics and business intelligence strategies, and communicate insights and recommendations to stakeholders.

According to PaySclae, as an entry-level Business Insights and Analytics Manager, with less than 1 year of experience, one can expect to earn an average total compensation of around 73,338 USD based on 7 salaries. Early in their career, with 1–4 years of experience, a Business Insights and Analytics Manager can expect to earn an average total compensation of approximately 88,718 USD based on 173 salaries. As they progress in their career, with 5–9 years of experience, mid-career Business Insights and Analytics managers can expect to earn an average total compensation of around 107,601 USD based on 413 salaries. Experienced Business Insights and Analytics Managers, with 10–19 years of experience, can expect an average total compensation of about 113,633 USD based on 195 salaries. In their later career, with 20 years or more experience, Business Insights and Analytics Managers can expect an average total compensation of around 120,822 USD.

This role is the missing part role in the majority of job reports in the Data Science discipline since it is a fairly new and recent job position. In the cargo ship example, the Data Ethics and Privacy manager is responsible for designing guidelines and procedures to cut CO2 emissions. In plain words, A Data Ethics and Privacy Manager is responsible for ensuring that an organization’s data collection, storage, and usage practices are in compliance with legal, regulatory, and ethical standards. They are responsible for developing and implementing policies, procedures, and guidelines to protect sensitive information and ensure the privacy of individuals whose data is being collected and used.

Since this role is a fairly new role in Data Science the salary data of this field is limited. According to PayScale, an early career Data Ethics and Privacy Manager, with 1–4 years of experience, can expect to earn an average total compensation of around 89,787 USD based on 30 salaries. As they progress in their career, with 5–9 years of experience, a mid-career Privacy Manager can expect to earn an average total compensation of approximately 98,322 USD based on 16 salaries. Experienced Data Ethics and Privacy Managers, with 10–19 years of experience, can expect an average total compensation of about 98,388 USD based on 19 salaries.

In conclusion, data engineering, data science, business insights and analytics management, and data ethics and privacy management are all critical roles within the field of data science and analytics. Each role has its own set of responsibilities, qualifications, and career development opportunities. Data engineers are responsible for building and maintaining the infrastructure that supports data science and analytics (I personally see data engineering jobs mostly leaning toward SW engineering, unlike the data scientists that they have a tangible outcome), data scientists use statistical techniques and advanced analytics to extract insights from data, Business Insights and Analytics Managers translate data insights into actionable recommendations to support decision-making and drive business growth, and Data Ethics and Privacy Managers ensure that an organization’s data collection, storage, and usage practices are in compliance with legal, regulatory, and ethical standards. Understanding the differences between these roles can help individuals interested in pursuing a career in data science and analytics make more informed decisions about which path to take. Please feel free to cite this blog if you use any part of this content and if you are interested to chat, shoot me an email at hello@nabi.me

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