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The need to bring several different disciplines together on one project gave rise to data science. Experts in the field work across mathematics, statistics and programming. The work itself involves collecting, filtering, analysing and then processing enormous volumes of information. Out of it the engineer produces forecasts, finds patterns and confirms their own hypotheses.

What kind of profession is data science engineering?

A data science engineer collects, processes and analyses data at scale, using machine learning and particular algorithms along the way. Their efforts go into finding patterns and trends inside enormous volumes of information, which makes it possible to predict how events are likely to unfold, optimise business processes or change direction. A data engineer:

processes unstructured data;
puts forward hypotheses;
analyses using various mathematical models;
delivers conclusions that are then applied in science, business, technology and elsewhere.

What does a data science engineer actually do?

What the job involves is unclear to many people. The work divides into these stages:

Collecting and processing data from various sources, filtering out likely errors and inaccuracies.
Exploratory data analysis (EDA): examining the information's structure, its distribution, possible correlations and outliers — results that stand out noticeably from the rest.
Machine learning, using the appropriate algorithms to build forecasts and models that work well.
Visualising the results, usually as charts and diagrams that make the processes easier for the client to understand.
Preparing an effective strategy and recommendations for what to do next, based on the results obtained.

The data engineer acts as an effective forecaster of processes. Taken seriously, the results of their work can be a substantial tool for a company's growth.

How a data science engineer works

To understand the work better, picture it as a chain:

Receiving a request from a manager.
Choosing suitable machine learning methods.
Building the model.
Determining which data to analyse and choosing the evaluation criteria.
Writing the algorithm and training the model.
Assessing how well the algorithm performs.
Helping with the rollout, refining and adjusting in light of the results.

An engineer's contribution lets a company optimise its processes and improve its products and services. It becomes easier to predict market demand and cut costs, which feeds into the strategic decisions taken.

How a data analyst differs from a data scientist

To avoid confusion, it helps to understand the difference. Their work is similar and both handle enormous volumes of data, but there are real distinctions:

An analyst runs statistical analysis to answer questions and solve problems. They collect data, find patterns and produce the report management needs for strategic planning.
A data scientist does more than collect, analyse and visualise data. They build a model of how things will develop from the information gathered, which takes machine learning and deep learning skills that ordinary analysts do not usually have.

How to become a data science engineer

There are several routes into the profession and into a role at a company of any size:

Studying data science engineering on the relevant courses at a university or college.
Taking a data science course at a specialist training provider and earning the corresponding qualification.
Taking the data science course remotely at PASV, earning a certificate and a placement on an internship.
Teaching yourself every aspect of the profession from the material available online. This is the longest and hardest road.

What matters is that the course covers current material and prepares you for the real problems you will meet.

What a data science engineer earns

The average annual salary for a data science engineer in the USA is $163,000. That is considerably above the national average, which sits around $120,000. Pay is shaped by experience, by skills and by the policies of the company you work for, as well as by the volume of work, the number of projects and how well they turn out.

The outlook for the profession

Data science engineering will remain in demand for a long while yet. Demand for people in this field has been growing steadily and will keep growing, along with the pay for qualified staff. What matters is getting good preparation so as to catch the attention of recruiters and large companies, and then continuing to develop your skills. There is no reason to expect demand to fall over the coming decade.