You are more than your DD-214.
Resumes, badges, and Linkedin profiles don't tell your story to employers. Shift does.
You're a gritty, cross-functional operator
You're a transitioning service member. You have spent eight years tackling open-ended problems in unstructured environments. You're an expert at cutting Gordian knots, learning and rapidly applying new technologies and systems, and building and improving processes.
You also happen to be an Army helicopter pilot.
But you want to transition careers — and you don't want to do the exact same thing in the civilian world that you did in the military.
Where do you start?
It can be hard to know where you fit
The hardest skills to measure are precisely the ones that don't show up on resumes. You know that you can add value to any organization, but you're not quite sure which roles make the most sense.
One thing is clear, though: you're not a cog in a machine. Any skills translator that boils you down to your military titles can't even begin to describe you.
You should look forward to Monday mornings*
Your job should engage your strengths, nourish your sense of purpose, and pose hearty challenges. Shift custom-tailors job recommendations based on your aspirations, skills, and personality.
Our matching process is bespoke.
Every candidate is different, and every job is different. Military vets are also different from the general population in certain ways — specifically when it comes to hard-to-measure soft skills. Any good matching algorithm needs to fold that information into its logic. At Shift, we consider a wide variety of information before matching you:
- work domain preferences
- soft skills
- hard skills
- personality traits
- workplace culture fit
- ...dozens (and counting) of distinct categories
Most job matching software relies on your military job title and an overly simple list of hard skills. If that were an adequate approach, then none of the following differences would matter:
Most skill translators assess you as an alphanumeric job code. We assess you as a human.
If the people in the graph above happened to have the same military occupational specialty, then most skill translation software would issue them the exact same career prospects. Pilots are funneled into airlines, and Special Forces soldiers are funneled into police and security guard jobs.
As an example, here's how most skill translation engines might summarize a mechanized infantry officer:
These skill translators are lazy. They lump you into groups based on occupational specialties, which prevents them from having to do the hard work of understanding you. You deserve better.
Shift learns about service members before matching
Shift matches you based on your preferences & aspirations, soft skills, hard skills, personality traits and strengths, values, and workplace culture preferences.
We also learn a lot about the roles. Our partner companies obviously want the best fits, too, so they collaborate with us to improve our matching process.
Here's some information we'd consider about a Shift fellow before conducting job matching:
Your preferences weigh heavily into the matching process
Studies have proven repeatedly that employee preferences are paramount. Employees whose preferences are aligned with their companies tend to be happier and stay with their companies for longer. We examine preferences that range from the domain you'd like to work in to the size and structure of your ideal company and its culture & values.
Getting technical: creating vectors and computing distance
Once we have the data we need about candidates and roles, we compare each candidate to each of our partner companies' roles. We create a vector for each candidate — one value for each dimension we care about. For example, if a Shift fellow indicates that she is particularly interested in sales roles, then her "sales" dimension would receive a large value. If a she prefers small organizations, then her "organization size" dimension would receive a low value.
Here's what a sample fellow's role vector might look like:
We create a similar vector for each role at each of our partner companies. The dimensions are comparable between roles and prospective Shift fellows.
Here's what a sample company's role vector might look like:
Visualizing distances between fellows and roles
Each vector describing a person or a role has dozens of dimensions. It's tough to visualize more than three to four variables, so we've used a technique called principal component analysis to smush about 50 dimensions into three: x, y, and z, which we can visualize on a 3D graph.
When we match, we use a distance function to generate a ranked list of fellows for each role. You can think of this step as drawing a line between blue and green dots.
- Each blue dot represents a role at one of our partner companies
- Each green dot represents a fellow (this is some of our actual fellows data, though it's anonymized)
- the shortest line from a person to a company indicates the strongest match.
This was a fairly high-level overview, but we hope that it communicated the important parts of our methodology. We're constantly iterating, so the specifics of our algorithm will change. One thing, however, will stay the same: our commitment to find you a job worthy of you.
Ready for a civilian career that fits you like a glove?
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