A digital twin is a live virtual model of a physical asset, line or facility, fed by real data from the thing it copies. Plant managers and operations leaders are hearing the term more often, usually from software vendors. The technology is real. The harder question is who will build it and keep it running.
Most stalled projects we hear about did not fail on the platform. They failed because nobody owned the data, or because the people who understood the equipment never talked to the people who understood the models.
What a twin actually needs from your team
Strip away the vendor language and a twin has three parts: a physical process, a model of it, and a steady flow of data between the two. Each part needs a different kind of person.
- The physical process needs someone who knows how it really runs: a maintenance lead, process engineer or production supervisor.
- The model needs someone who can build and tune it: a data scientist, simulation engineer or machine learning engineer.
- The data flow needs someone who can connect sensors, controls and storage: a data engineer, controls technician or IoT specialist.
If one of the three is missing, the twin becomes an expensive dashboard.
Start with one problem, then hire for it
The common mistake is buying the tool first and looking for a use after. Reverse that. Pick one costly problem on your floor or in your network and size the team to that.
Good first candidates for a mid-sized manufacturer or operator:
- A line or machine with repeat unplanned downtime
- A bottleneck where throughput swings and no one can say why
- An asset class where maintenance is done on a calendar, not on condition
- A warehouse or yard flow that backs up at predictable times
A narrow target keeps the first hire list short. You may need two or three people, not a department.
The data work comes before the modelling
A twin is only as good as what feeds it. Many plants have sensors that were installed years ago, log in different formats, and have never been checked against actual readings. Before you pay for simulation talent, find out:
- Which machines are instrumented, and how reliable the readings are
- Where the data lands, and who can access it
- Whether tags, units and asset names are consistent across sites
- Who is allowed to change the data and who signs off on quality
This is why the first hire is often a data engineer rather than a data scientist. A modeller with bad inputs will produce confident, wrong answers.
The roles that are hard to find
The scarce profile is not a pure software person. It is someone with enough operations background to question the model. Plants that hire only from the technology side tend to get models the floor does not trust. Plants that hire only from the operations side get good instincts and no way to scale them.
When you write the role, ask for both:
- Experience on a real production or infrastructure environment, not only cloud projects
- Comfort with historians, PLC data or SCADA outputs alongside Python and cloud tools
- Ability to explain a result to a shift supervisor in plain terms
Expect these hires to take longer than a standard analyst role in both Canada and the US, since the pool sits between two trades. Build that into the project timeline.
Contract first, permanent later
A first twin is a project with a defined start, and you may not know yet what the long-term team looks like. Contract specialists can cover the build: integration, data cleanup, model development. Meanwhile you hire permanent owners for what must stay in house, such as the operations lead who owns the use case and the engineer who maintains the data pipeline.
That split limits your risk. If the pilot shows value, you convert or add. If it does not, you have not carried a full team for a tool you dropped.
Do not forget the people who run the plant
A twin changes how maintenance and planning decisions get made. Technicians and supervisors need to see it as a help, not an audit. Involve them in the pilot and give a named person the job of translating results for the floor. We have seen the same pattern in other change projects, including a food manufacturer in the GTA that made over 20 hires in 18 months across production, skilled trades and office roles: the roles that connect groups matter as much as the technical ones.
What to do next
Before you evaluate any platform, write down one problem, the data you have for it, and the three people who would need to own the model, the data and the process. If you cannot name them, that is your first hiring gap. A Traverse recruiter can help you scope those roles and find contract or permanent people for them.