Your recruiter sent a CCAT link for a data science role, and now you want one straight answer: what score do you actually need to hit? The honest version is that no company publishes a fixed number, but data and analytics roles consistently sit near the top of the CCAT scale, and there is a defensible target range you can aim at. This page gives you that range, explains why data roles are held to a higher bar than most, breaks down which question types carry the most weight for you specifically, and shows you how to close the gap before test day.
First, a quick disambiguation so no wrong facts sneak in: this is the Criteria Cognitive Aptitude Test, the workplace hiring test from Criteria Corp, not the Canadian Cognitive Abilities Test used in school admissions for children. Same acronym, completely different test.
Quick takeaways
- Most data science roles target roughly the 75th to 90th percentile, a raw score around 31 to 35 out of 50.
- The CCAT average is 24 correct (50th percentile), so a “fine for everyone” score is usually not enough for a data role.
- No company publishes an official cutoff. Ranges are informed estimates from role difficulty and reported cutoffs, not guarantees.
- Math and logic plus spatial and abstract reasoning tend to carry the most weight for data roles, because they mirror the actual work.
- There is no penalty for wrong answers, so you should never leave a question blank.
- Which points you are losing, and why, matters more than the target number itself.
The short answer: the target range for data scientists
If you want a single number to aim at, aim for the top 15% of test-takers, which is a raw score of about 31 to 35 correct out of 50, roughly the 80th to 90th percentile. Clear the low end of that band and you are competitive at most companies hiring data scientists. Reach the high end and you clear the bar almost everywhere, including firms that set an aggressive cutoff.
Why that range and not lower? Because the CCAT is a general-population test, and the commonly cited average score of 24 (widely reported as the 50th percentile) represents the whole applicant pool across every kind of job. Analytical roles filter above that. A data scientist’s day is pattern recognition, quantitative reasoning, and quickly learning unfamiliar systems, which is close to exactly what the CCAT measures, so employers hiring for those roles tend to weight the score heavily and set the cutoff high.
Be clear-eyed about one thing: no employer publishes “you need a 33.” These ranges come from the general difficulty tier of analytical roles and from cutoffs candidates have reported, not from an official Criteria Corp table for data scientists. Treat 31 to 35 as a well-supported target, not a printed rule.
How data science compares to other roles
Data science sits at the top end of the CCAT expectation curve, alongside consulting and just above general software engineering. Here is how the common target bands line up so you can see where you stand relative to adjacent roles.
| Role | Typical target percentile | Typical raw score band |
|---|---|---|
| Administrative / support | ~30th to 50th | 20 to 24 |
| Sales | ~40th to 60th | 22 to 27 |
| Software engineer (general) | ~65th to 85th | 28 to 33 |
| Data scientist / analytics | ~75th to 90th | 31 to 35 |
| Consulting | ~75th to 90th | 31 to 35 |
| Executive / leadership | ~80th to 92nd | 32 to 36 |
Two things worth noticing. First, data science asks for a couple more correct answers than a general software role, which is a meaningful gap given how tightly scores cluster near the average. Second, the difference between “average” and “data-role target” is only about seven to eleven more correct answers, but on a 50-question, 15-minute test, those points are hard-won. That is the whole challenge in one sentence.
For the tech-role picture in full, see CCAT scores for tech jobs, and for the closely related engineering benchmark, CCAT scores for software engineers.
Which question types matter most for data roles
The CCAT reports a single composite score, so technically every question counts the same toward your raw number. But for a data-role candidate, the strategic reality is that the quantitative and abstract sections are both where you are expected to be strong and where you can gain or bleed the most points. The test breaks down roughly like this:
| Question domain | Approx. share of the 50 | Why it matters for data roles |
|---|---|---|
| Math and logic (word problems, number series) | ~34% | Direct proxy for the quantitative reasoning data work demands |
| Verbal reasoning (analogies, antonyms) | ~34% | Tests how fast you parse and reason with information |
| Spatial and abstract reasoning (patterns, outliers) | ~22% | Pure pattern recognition, the core cognitive skill of analytics |
For a data scientist, math and logic plus spatial and abstract reasoning together are close to two-thirds of the test, and they are the sections that best mirror the actual job. If you are already comfortable there, the CCAT rewards you. If your quantitative speed is rusty, or you have not seen abstract pattern questions since school, that is precisely where a data-role candidate loses the points that separate a 27 from a 33.
One honest caution: do not neglect verbal. It is a third of the test, and a data candidate who assumes they will coast on the math half and ignores verbal often leaves easy points on the table there.
A worked example: pacing to a data-role score
Here is what hitting the target actually looks like against the clock. You have 50 questions and 15 minutes, which is about 18 seconds per question. Less than 1% of people finish all 50, so the goal is never “answer everything.” The goal is “convert as many as possible into correct answers before time runs out.”
Say you are aiming for a raw score of 33 (roughly the 85th percentile). A realistic path: you work steadily and get through 42 questions in the 15 minutes, banking correct answers on the ones you know cold and making educated guesses on the two or three that are eating your clock. Of those 42 attempts, you convert 33 into correct answers. That is your 33. Notice you did not need all 50: you needed accuracy on the ones you touched plus enough pace to reach question 42.
Now the failure mode. Same person panics, tries to rush all 50, and lands 30 correct out of 48 attempts because accuracy collapsed under speed. Raw score 30, about the 78th percentile: still decent, but you just dropped from the strong-data band to the edge of it by rushing. On the CCAT, and especially for a data role where you need the higher end, controlled pace beats frantic speed every time. And because there is no penalty for wrong answers, the one thing you must never do is leave a question blank when time is nearly up: guess on everything unanswered in the final seconds.
How to close the gap to a data-role score
If you sit a practice test and land at, say, the 60th percentile when your target is the 85th, the useful question is not “how do I get better generally.” It is “which specific question types and which specific habit (speed or accuracy) are costing me the points.” For a data-role candidate that answer is almost always concrete, and concrete is what you can fix.
This is exactly what CCAT Tests is built to surface. Your practice results come back as a heat-map of the question types slowing you down, math and logic, verbal, or spatial and abstract, plus drills aimed at your weak areas. For a data candidate that usually pinpoints something specific and fixable, like “you lose four points a test to abstract pattern questions and two more to careless arithmetic under time pressure.” That is the kind of weak-area analytics that team assessment tools charge hundreds a month for, packaged into a one-time purchase.
It also matters that the practice bank mirrors the real CCAT, including the spatial and abstract-reasoning images that generic aptitude sites tend to skip entirely. For a data role where those pattern questions are a fifth of the test and one of your expected strengths, practicing on filler that does not look like the real thing is worse than not practicing. You can start on the free tier, sit a real practice test across all three domains, and see where you actually stand against that 31-to-35 target before deciding whether to pay for anything.
FAQ
What CCAT score do data scientists need?
There is no official published cutoff, but data science roles typically target the top 15% of test-takers, roughly a raw score of 31 to 35 out of 50 (about the 80th to 90th percentile). Clearing the low end makes you competitive at most companies; the high end clears the bar almost everywhere.
Is 35 a good CCAT score for a data scientist?
Yes. A 35 sits around the 90th percentile and is a strong result for a data role. It clears the target range at essentially every company hiring data scientists, including those with aggressive cutoffs.
Is 26 out of 50 good for CCAT?
For the general population, 26 is just above average (around the 58th to 60th percentile). For a data science role specifically, it usually falls short of the target band, since data roles tend to look for the 80th percentile and up. It is a fine general score but a below-target one for analytics.
What is a passing score for the CCAT?
There is no universal passing score. Each employer sets its own cutoff per role. For data science, the practical target is roughly 31 to 35 correct answers, but the only cutoff that truly matters is the one the specific company you applied to has set.
What is the average score on a CCAT test?
By commonly cited figures, the average is around 24 correct answers out of 50, which is widely reported as the 50th percentile. Data and analytics roles almost always expect a score well above this average.
Which CCAT question types matter most for data roles?
All questions count equally toward your raw score, but math and logic plus spatial and abstract reasoning are the sections that best mirror data work and where strong candidates gain the most ground. Together they make up about 56% of the test. Do not neglect verbal, though, since it is another third.
Is the CCAT like an IQ test?
The CCAT measures cognitive aptitude (problem-solving, learning speed, critical thinking) and overlaps with what IQ tests measure, but it is a pre-employment aptitude test, not a clinical IQ assessment. Employers use it to predict on-the-job learning and problem-solving, especially for analytical roles like data science.
Related on CCAT Tests
- Role deep dive. CCAT scores for software engineers. The closely related engineering benchmark and how it differs from data roles.
- Role deep dive. CCAT scores for tech jobs. The full tech-role picture across job titles.
- Pillar. CCAT scores and results guide. How to read, compare, and improve your CCAT results.
- Practice. Free CCAT practice tests. Sit a real practice test and see where you stand against the data-role target.
Try CCAT Tests
Find out exactly where you stand against the data-role bar.
Knowing the target is 31 to 35 is step one. Knowing whether you are hitting it, and which question types are holding you back, is what actually gets you there. CCAT Tests gives you a heat-map of your weak areas and drills to fix them, on a real CCAT-style question bank that includes the spatial and abstract images data roles lean on. The free tier is a genuine practice run across all three domains, so you can benchmark yourself before deciding whether to pay for anything. Data roles often stack the CCAT with other assessments, so if you need broader coverage, the full PrepClubs platform preps you for the CCAT and 20+ other pre-employment tests with a pass guarantee.
