Hi, and welcome to today’s video, where we explain the different types of data
science jobs in just five minutes. When looking for a job in this field,
remember that working with data is a metaskill; there’s not only one specific skillset required
to succeed in this line of work. Companies recruit for various roles,
and each of these positions requires different competencies. So, our aim today is to describe
who does what and how data professionals with different job titles contribute to a business.
Ok. Are you ready? Let’s start the clock, then.
Ideally, before a company collects any data, it hires a data strategist—a senior professional
who understands how data can help a business in a specific industry. According to famous data
strategist Bernard Marr, there are four main ways how a company can take advantage of data:
1. It can use data to make better-informed decisions.
2. Data can help a company offer smarter products and services.
3. Data allows a company to improve business processes.
4. It could create a new revenue stream via data monetization.
Sometimes companies hire an external consultant for this role to envision how
data will create business value by devising a plan that puts data strategy in sync with
the firm’s overall business strategy. Once a firm has a data strategy in place,
it is time to ensure data availability. This is when a data architect’s role is needed.
A data architect (or data modeler) plans out high-level database structures and foresees
the needs of business stakeholders to ensure an optimal database schema.
Without proper data architecture, an organization could end up in a situation where people from the
business ask questions that cannot be answered because there are tables in the database that
don’t talk to each other. Again, a data architect is a senior professional and often a consultant.
Companies rely on data engineers to execute the plans created by data architects. A data engineer
is mainly focused on building the necessary data infrastructure by organizing tables and setting
up the data to match all the use cases defined by the data architect. They handle the so-called
ETL process, which stands for Extract, Transform, and Load data. It consists of retrieving data,
processing it in a usable format, and moving it to a repository (the firm’s database). So,
data engineers are responsible for piping data into tables correctly. Typically,
they receive many ad-hoc ETL-related tasks throughout their work but rarely interact with
business stakeholders directly. You need software engineering skills to work as a data engineer.
But remember that the role of data engineers and data architects often
overlaps—especially in smaller businesses. All right. Let’s briefly pause the clock before
continuing with data science professions. Please like this video and subscribe to our
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training that will get you job-ready. 😊 Great! Let’s continue. The next position
on our list is the data analyst. After data has been made available
in a firm’s database, it’s up to data analysts to explore, clean, analyze,
visualize, and present the information to provide valuable insights for the business.
A data analyst typically uses SQL to access the database;
they leverage an object-oriented programming language like Python or R to clean and analyze
information and use such visualization tools as Power BI or Tableau to present findings.
Another compelling position, like the data analyst role, is the Business Intelligence
(or BI) analyst. Data analyst and BI analyst duties overlap to a certain extent. Still,
the main difference is that BI analysts tend to focus on building meaningful reports and
dashboards, which are updated frequently and satisfy stakeholders' informational needs
at different levels of the organization. We could say that a BI analyst has more of
a reporting role than a data analyst. A data scientist, on the other hand,
is a data professional with the skills of a data analyst but can leverage machine and deep learning
to create models that can use past data to make predictions. We can distinguish three
main types of data scientists: - Traditional data scientists
- Research scientists - and Applied scientists
Traditional data scientists are generalists who engage in all data science tasks,
including data exploration, advanced statistical modeling, experimentation via A/B testing,
and building and tuning machine learning models. Research scientists are specialized data
scientists that large companies primarily hire to work on developing new machine learning models.
Applied scientists—frequently hired in big tech and larger companies—boast one of the
highest-paid roles in data science. An applied scientist can combine data science and software
engineering skills to productionize their model. More prominent companies prefer this combined
skillset because it allows one person to oversee the entire ML implementation process—from the
model building until productionization—which leads to quicker results. An applied scientist
can work with data, model it for machine learning, select the correct algorithm,
train the model, fine-tune hyperparameters, and then put the model in production.
Companies that don’t have applied scientists hire ML Ops engineers responsible for putting
the ML models prepared by traditional data scientists into production. In many instances,
an ML Ops engineer is one who was formerly a data scientist and decided to develop an engineering
skillset. ML Ops engineers focus on putting the ML model in production. If something breaks with
the machine learning model in production, ML Ops engineers are the ones who try to fix it.
The last data role to explore is the data product manager. A product manager
is accountable for the success of a data product. This professional considers the
bigger picture and strategizes the most effective ways to build a data
product. A product manager is accountable for the success of a data product. They identify what data
product needs to be created, when to build it, and what resources are necessary. A significant
focus of a data product manager is data availability—determining whether to collect data
internally or find ways to acquire it externally. And that’s it—the different types of data science
jobs explained in about five minutes 😊. We tried to win the race against the clock,
but, more importantly, we hope you learned something new.
Let us know how we did in the comments and what you’d like to see next. 😉