Logistician vs data scientist: which profession has a future in the USA?

Both data science engineering and logistics are promising, in-demand professions in the USA and worldwide. Both involve working with large volumes of data, and the job in each case is to optimise processes. While the logistician attends to the efficiency of deliveries, the data science engineer works on algorithms that improve business processes. There are rather more differences than similarities, though, and those are what we go through here.

Logistics in the USA

Any business dealing in supply and goods needs a logistician. The work is available across sectors, at small companies and large corporations alike. Conditions are usually standard: a logistics dispatcher in the USA works remotely or in an office on weekdays. There are occasional site visits, so even someone working from home should live reasonably near the firm.

Look at the vacancies and here is what is usually asked of a logistician:

• choosing the best way to move a load;
• planning routes;
• managing stock and monitoring quantities;
• dealing with carriers, suppliers and clients;
• analysing freight data;
• working in logistics software.

The career is an interesting one, but the particulars deserve saying:

1. You work to firm deadlines. Delays, errors and inattention mean losses, and the logistician answers for them.
2. Questions have to be resolved quickly. There is minimal time to think while deadlines burn and delays feed into the routes.
3. A logistician has to coordinate a great many processes at once and stay engaged constantly.
4. A good deal of the work is routine and repetitive.
5. Automation is reducing the need for ordinary logisticians.

We will come back to prospects shortly. First, what a data science engineer does.

Data science in the USA

A data scientist is a company's guide, in a sense. They work with all the data available, analyse it and build predictive models from the results. With a data science professional on staff, a company finds it easier to make well-founded decisions and run the business properly.

This is a promising profession in the USA and it is in demand across a wide range of fields. You can choose medicine, e-commerce and marketing, concentrate on finance or join a tech company — America offers no shortage of ways to realise your potential.

What a data scientist is asked to do:

• collect, process and analyse data;
• work with AI and build machine learning algorithms;
• work with cloud services, databases and analysis tools;
• visualise data and present the results of their work.

Another advantage is how the work is arranged: hybrid, remote on staff, freelance and so on. You can be part of international projects while sitting on the other side of the ocean from the firm. Data science engineers also develop constantly, learning new tools, approaches and methods, so there is rather less routine here than it first appears.

As for the drawbacks:

1. A high barrier to entry. You have to learn machine learning, programming, statistics and mathematics.
2. Competition. America offers specialists remarkable opportunities, so no shortage of people want to become data science professionals. Getting into the field takes work.
3. Continuous learning. Staying in demand means constantly learning something new.

Courses, new algorithms and keeping up with the trends are part of the job anywhere in tech. But as your skills grow, so does the salary — which brings us to the money.

Comparing the salaries

We looked at the US market. A logistician's salary depends largely on experience:

• Someone starting out earns $45,000 to $60,000 a year.
• An experienced logistician can expect $70,000–100,000 a year.
• A director of logistics earns upwards of $120,000.

Now the vacancies for data scientists in the USA:

• Junior data scientists are offered $100,000–120,000 a year.
• Mid-level data scientists earn $130,000–160,000 a year.
• More experienced specialists command at least $180,000 a year.

Logistics is in demand, but the money in it is lower than in tech. Pay rises with experience, but the same holds as you advance in data science.

What lies ahead for each profession

Technology has the greatest influence on the job market today, and that will hold in future, which will show in logistics and in tech alike. Anyone interested in a logistics career should get closely acquainted with automation now, and here is why:

1. AI and big data are increasingly used to forecast supply and optimise routes.
2. Modern companies build automated warehouses and robotic delivery systems.
3. Blockchain is used to track supply chains.

Staying competitive means being able to direct robots and algorithms — which makes it time to learn digital technology.

A data science career, by contrast, looks more promising. As technology develops, there will be more and more data. Employers will want people who can use it properly and turn it into valuable business decisions. Automated analytics, the adoption of AI and neural networks all work in engineers' favour.

Frequently asked questions

Is becoming a logistician hard?
The barrier to entry is comparatively low: you can take a specialist course or start as a dispatcher and work your way up.

How long does it take to become a data science engineer from scratch?
Six months to two years, depending on the format and intensity of the training.

Can data science be done remotely?
Yes.

Is it hard to move into data science from another part of tech?
No. Existing tech knowledge is an advantage and the learning comes considerably more easily than it does to complete beginners.

Do data science engineers need certifications?
Yes. A certificate confirms your skills and improves your chances of getting hired.