The Academic CV Element Reference, Kept, Cut, or Translated

You have a PhD or a postdoc CV, a target job that is not another postdoc, and a page of career advice that keeps telling you to "translate research into impact" and "cut to one page" without saying what to do with your teaching section, your service, or your fourth-author paper. This guide is the lookup table those articles skip. It goes one academic CV element type at a time and, for each, tells you what an industry reader infers, how the element misleads, whether to keep, cut, or translate it, and where the survivor lands on a one-page resume.

Read it row by row. Jump to the section in front of you, make the call, and leave.

Why an academic CV and an industry resume are different documents

An academic CV is a complete, unlimited-length record of academic achievement; an industry resume is a 1 to 2 page argument for a specific job. They are not long and short versions of the same thing. The CV proves you belong in the field. The resume proves you fit one posting.

Length scales with academic seniority but the resume target does not move.

CV page counts are from published length guides; the resume target is from university career centers. The gap is the point: an assistant professor is discarding 6 to 13 pages of content, and the discipline is deciding which elements survive the cut, not shrinking every element by the same fraction.

The resume is also tailored to one job, optimized to pass an automated keyword screen, and focused on quantified business impact. That is three constraints the CV never had. So the conversion is not editing. It is a per-element re-decision.

The disposition rule: keep, cut, or translate

Every academic CV element gets exactly one of three dispositions. Keep means it survives roughly as-is, in a section a corporate reader expects. Cut means it comes off the resume entirely (it can live in a linked profile). Translate means the content stays but is rewritten into an outcome bullet under a corporate heading.

The disposition of most elements flips on one variable: your target track. A research-heavy target (Research Scientist, Data Scientist at a research lab, R&D) expects a detailed record. A general corporate target (product, consulting, operations, analytics) treats that same record as friction unless it directly supports fit. The same resume that lands a postdoc interview will fail a Senior Data Scientist screen because the bullets were tuned for the wrong audience.

This matters more than it looks, because the "safe" default drifts the wrong way.

The supply picture also differs by country, which changes how much translation work pays off.

Counts are from Refolk's index; ratios are derived from those counts. A UK PhD converting to industry works from a thinner peer pool - roughly a third of the US Data Scientist population - which means fewer local resume exemplars and a higher payoff from explicit, jargon-free translation. If you cannot copy a peer's structure, you have to build it deliberately.

The element-by-element reference

Below is the row-per-element table. Find the section in front of you, read the inference and the misfire, take the disposition, and note the placement. Track-dependent rows say so.

Publications

An industry reader infers depth and independence from a publications list, but also reads it as a signal of which track you are chasing. That is its danger: it is a track-detector, not a neutral credential. A long list tells a product hiring manager you may not want a product job.

All three rows are from published publication-listing guidance. The threshold is firm: 3 or more relevant papers earn a dedicated section; 1 or 2 do not, and a one-item section reads as padding. On a one to two page resume, list 3 to 5 under a Selected Publications heading, ordered by relevance, then recency, then venue prestige. Use the word "Selected" whenever the full list runs past five, because it tells the reader the list is deliberately curated rather than truncated. For research-heavy targets, keep the section. For corporate targets, shrink it to a single line or a link and reclaim the space.

Dissertation or thesis

An industry reader infers your core research competency and the shape of your problem-solving from the dissertation. It misleads when it sits under Publications: it implies a peer-reviewed record you may not have. The disposition is keep, translate placement. For a PhD candidate the dissertation always belongs in the Education section, at minimum as degree, institution, and dissertation title. Only list it under Publications if it was actually published in a journal. If it produced separate articles, those articles are your publications and the thesis stays in Education.

Research experience

An industry reader infers your methods, tools, and whether you can own a project end to end. It misleads when it is written as academic narrative, because the reader cannot extract an outcome. Disposition is always translate. This is the load-bearing conversion: research experience becomes your Experience section, rewritten into quantified bullets. Placement is the main body, directly after Education for recent graduates, or after a Skills band for corporate targets.

Grants and fellowships

An industry reader infers that independent evaluators funded your judgment, which reads as credibility and competitiveness. It misleads when listed as a long ledger of small internal awards, which looks like padding. Disposition is translate and compress: keep the two or three most competitive and fold them into a bullet ("secured $X in competitive funding across two national programs") rather than a standalone list. Note that NSF and NIH biosketches are generated through SciENcv and are a separate academic artifact; do not import biosketch formatting into a resume.

Teaching experience

An industry reader infers communication, mentoring, and the ability to explain complex work to non-experts - genuinely valuable corporate skills. It misleads when listed as a course catalog, which signals you are aiming at faculty roles. Disposition is translate for corporate targets, keep for teaching-adjacent or research targets. Translate into one or two bullets about scale and outcome ("designed and taught a 120-student course; redesigned assessment, cutting grading time 40%"), not a list of course numbers.

Conference talks and presentations

An industry reader infers you can stand up and present to a room, which maps to stakeholder communication. It misleads when the full talk history runs a page: nobody in industry reads it. Disposition is cut most, translate the rest. Collapse the whole history into a single line ("presented at 8 international conferences") or one bullet, or drop it entirely and let a linked profile hold the record.

Academic and university service

An industry reader infers collaboration, initiative, and organizational skill from service - review work, committee membership, event organizing. It misleads badly when listed by its academic names, which read as invisible unpaid labor to a corporate reader. Disposition is translate the leadership-flavored items, cut the rest. "Organized department seminar series" becomes a bullet about coordinating speakers and logistics. Journal reviewing becomes one line, not a list. Placement is a compressed bullet under Experience or a Leadership line, never a standalone Service section.

Advising and mentoring

An industry reader infers people-leadership and the ability to grow others, which is management signal. It misleads when it is a single advisee reading as a thin one-line section. Disposition is translate and fold in. "Advised 6 graduate and undergraduate researchers" becomes a mentoring bullet inside your research role. Do not make a section for it.

Professional affiliations and references

An industry reader infers almost nothing from a list of society memberships, and infers wasted space from "References available on request." Disposition for both is cut. Memberships come off unless one is directly relevant to the target. References are provided when asked, not printed.

The publications section is a track-detector, not a credential: mandatory for a research role, a liability for a product one.

The scan the survivors have to pass

The first pass on your resume is a fast fit or no-fit decision, and it concentrates on a fixed set of items. This is why placement matters as much as content. It is not a reason to gut the resume.

The famous six-second figure comes from one 2012 eye-tracking study of about 30 recruiters. The behavior is real; the number is not law. A 2018 follow-up put the initial screen at 7.4 seconds. A 2025 analysis of 4,289 resume reviews found the scan runs 11.2 seconds when the job description is visible side by side, and the 6-second figure only holds without that context. Hold the number loosely and trust the finding underneath it.

That finding is what gets looked at. The original study found 80% of initial review time went to six items: name, current title and company, previous title and company, current dates, previous dates, and education.

The practical instruction: put your title and your strongest impact in the top third and along the left margin, where reading time concentrates. Keep your depth below that, because it gets read once you clear the first pass. Deleting content to "respect" the six-second rule is the mistake; the rule rewards front-loading, not cutting.

The procedure: run the whole CV to a converted resume

Work the seven steps in order the first time. The one genuine sequencing debate is whether to fix formatting first or last; the safer path is content first, format last, because a clean single-column layout of the wrong content still fails.

Budget roughly a day of focused work: the inventory takes about half an hour, the per-element decisions one to two hours, and the bullet translation the biggest block at two to three hours. The translation is where most of the value lands, because it is where CV jargon becomes something a non-specialist can act on.

Tailoring the bullets to each posting is the part that does not scale by hand, since the disposition flips with the target track. This is exactly the friction Refolk removes: it writes the resume from your own history and re-tailors it to each posting, so you decide keep, cut, or translate once and let the tailoring adapt per application.

How to write a translated bullet

A translated bullet has three parts: an action verb, the context (what you did and how), and the result. At minimum, state the outcome, impact, or implication even when you cannot attach a number. Quantify with how many, how much, or how often whenever the number ties to an outcome.

Where there is no revenue figure - which is most academic work - use proxy metrics. Citation count and audience size are legitimate denominators. The task is finding a real one, not inventing a fake one. Compress each role into 2 to 4 quantified bullets.

CV form (do not ship):
"Conducted research on protein folding using molecular dynamics simulations."

Translated:
"Built molecular dynamics pipeline in Python to model protein folding, cutting simulation setup time from 3 days to 4 hours across a 12-person lab."

CV form (teaching):
"Taught undergraduate statistics."

Translated:
"Designed and taught statistics course for 120 students; rebuilt assessment workflow, reducing grading turnaround 40%."

Notice what changed. The tool is named. The scale is stated. The outcome is something a non-specialist can picture. A bullet that says "ran 300 PCR reactions" fails this test, because the number is volume with no impact behind it.

How the conversion goes wrong

Most failed conversions share a small set of failure modes, and several are false positives - things that look correct and are not. Check each before you call the resume done.

• A "Selected Publications" list that is not selective. Pasting the full CV list under the word "Selected" defeats the signal; it looks curated but runs fifteen lines. Check: count the entries and cap at five.
• The two-column "ATS-friendly" template. It looks clean to a human and the PDF opens fine on your screen, but a strict parser drops the right column. Check: run a parse test and confirm every field extracts, not just that the file opens.
• Trusting the recruiter to translate. The resume left in CV form assumes someone will decode your jargon. They will not. Check: could a non-specialist name the outcome of each bullet?
• Meaningless numbers. A volume metric like "ran 300 PCR reactions" quantifies effort, not impact. Check: does the number tie to a result - time saved, error reduced, adoption gained?
• Thin one-item sections. A single publication or one advising line reads as padding. Check: apply the 3-or-more rule; otherwise fold it into a bullet.
• Over-trusting the six-second stat. Treating it as literal law leads to gutting real content. It measures triage, not reading. Check: the top third carries the fit signal, and the depth stays below it.
• The dissertation filed as a publication. If the thesis was not published in a journal, it belongs in Education. Check: is it peer-reviewed? If not, move it.

The formatting failure deserves special weight because academics import exactly the structure that breaks it. Parsers read a document as a continuous linear stream, left to right and top to bottom. A table asks the software to read a two-dimensional grid, and side-by-side columns scramble into unreadable word salad. The multi-column aesthetic that makes a CV look polished is the thing that fails the screen.

Where to see the destination before you build it

Before you finalize placement, it helps to see what your conversion looks like once it lands - the actual titles, employers, and paths of people who made the same move from your field.

In Refolk's index, Meta is a top-three employer for PhD-holding Data Scientists in both the US and UK and for US Research Scientists, so these are not edge cases. The profiles show you which of your elements survived on the resumes that worked, and where they landed.

Final check before you submit

Run this list against the finished file. Each item is a specific verification, not a topic to think about.

Keeping the resume current

The disposition of an element is not permanent - it depends on the track you are targeting today. When you switch from a research target to a corporate one, the publications section flips from keep to cut before anything else changes, so re-run the per-element decision against each new posting rather than reusing yesterday's file. Keep your full academic record intact in a linked profile such as ORCID or Google Scholar, so cutting an element from the resume never means losing it. When a new posting arrives, pull its keywords, re-decide the borderline elements against that track, and re-parse-test before sending. The inventory only has to be built once; the keep, cut, or translate call is what you refresh per application.