31.10.26

How Dcoded Prepares Data and AI Candidates for Technical Interviews

When a hiring manager tells us "we need someone who actually understands the stack, not a CV that name-drops it," we don't treat that as a sales objection to overcome — we treat it as the brief. Every candidate we put in front of a client has already been through a structured technical briefing process that mirrors the client's own interview format, scorecard and all. This guide walks through exactly how we do it, using our work preparing candidates for Adjust AI's technical interviews as the working example.

What You'll Learn

This guide sets out the step-by-step process Dcoded uses to prepare Data and AI candidates ahead of client technical interviews — from sourcing the client's actual scorecard to coaching engineering fundamentals like SOLID and DRY. For hiring managers running rigorous technical processes, the goal is simple: reduce the risk of a mismatched-fit hire by making sure every candidate arriving at your interview has been genuinely assessed against your competencies, not just matched against your job spec's keywords.

Prerequisites: What We Need From a Client Before Briefing Begins

Before we can prepare a candidate properly, we need the following from the client:

• The actual interview format — conversational, technical, or a mix — so coaching reflects reality rather than guesswork

• A copy of or detail on the scorecard being used to mark candidates, including the named competencies

• Specifics on which technical areas will be probed in depth — for example, a named focus on.NET and.NET Core

• Confirmation of whether the interviewer will review a candidate's own code or GitHub project as part of the technical conversation

With that information in hand, we can move to the preparation steps themselves.

Step 1: Secure the Client's Scorecard and Interview Format

The first step is never generic "interview tips" — it's getting hold of the actual competency scorecard the client interviewer is marking against. In our work with Adjust AI, we obtained the scorecard the hiring interviewer was using and found ten distinct competencies on it, including values and alignment, communication and collaboration, role fit, project history and experience, engineering fundamentals, and a competency dedicated entirely to the client's core stack — in that case, C# and.NET. We also confirmed the shape of the conversation itself: a conversational opening covering the candidate's profile and the business, before moving into a technical phase that goes deep, quickly.

Note: Treat the scorecard as the brief. Coaching without it is coaching blind — you're guessing at what matters to the interviewer instead of knowing it.

Step 2: Brief the Candidate on the Business, Not Just the Role

Before any technical coaching happens, we send candidates a business pack — background on who the client is, what they do, and how the business owner describes the mission, built directly from material the client provided. This matters because one of the scorecard competencies in the Adjust AI process was "values and alignment," specifically probing why a candidate wants to join a business at that stage — in that case, a startup — rather than a more established employer. A candidate who can speak credibly to that competency, because they've actually understood the business rather than skimmed a careers page, scores differently than one who hasn't.

Step 3: Map Each Scorecard Competency to Candidate Prep

Once we have the scorecard, we work through each competency individually rather than treating interview prep as one generic conversation. In the Adjust AI case, that meant covering:

• Values and alignment — preparing the candidate to speak to their motivation for choosing a startup path over a safer one

• Communication and collaboration — coaching on how the candidate comes across and what evidence they can point to

• Role fit — matching the candidate's tech stack explicitly to the business's stack

• Project history and experience — making sure the candidate is ready to talk through any project in detail

• Engineering fundamentals — covering SOLID, DRY, and more niche areas like the Law of Demeter and the test pyramid

• Security fundamentals — in that case, expected to focus on securing web APIs

• Stack-specific depth — an entire competency dedicated to C# and.NET

This mapping exercise is the mechanism, not a slogan: it's how we make sure a candidate isn't simply "good at interviews" but prepared against the specific dimensions the client is actually marking.

Step 4: Coach Engineering Fundamentals Directly

Engineering-fundamentals questions are where keyword-matching candidates get exposed fastest, so we coach these explicitly rather than assuming a strong CV implies strong fundamentals. For the Adjust AI brief, that meant a direct conversation about SOLID and DRY principles, flagging the Law of Demeter as a more niche concept worth a refresher, and recommending the candidate revisit the test pyramid in case it came up. In a separate Adjust AI interview, the technical emphasis sat heavily on.NET and.NET Core — how long the candidate had used it, which projects they could speak to, and readiness for follow-up questions probing their initial answers.

Tip: Where a candidate's most recent hands-on experience is in an adjacent language — TypeScript rather than.NET, for example — we coach them to bridge that gap explicitly in the interview rather than hope it goes unnoticed, drawing out the conceptual overlap between the two.

Step 5: Prepare for Code-Review Exercises

Several client interview formats we prepare candidates for include a live code-review element, and we make sure candidates know this is coming rather than discovering it in the room. In the Adjust AI process, the interviewer was known to pull up code and ask the candidate what they would change, and separately to use a candidate's own submitted GitHub project as the basis for technical discussion. We confirm in advance which of a candidate's projects the interviewer has already reviewed, so the candidate can walk in ready to talk through their own code and defend the decisions behind it.

Step 6: Share Real Interviewer Feedback to Build Confidence and Precision

Part of what separates a genuine briefing from a generic pep talk is feeding back specific, real signal from the client side. Ahead of one Adjust AI interview, we relayed that the interviewer had been impressed by the candidate's tailored cover letter — a rare thing in his experience — and had specifically flagged a concrete technical example in the candidate's CV, a batch process reduced from 100 hours to three, as a strong interview talking point. We also flagged that the interviewer had noted the candidate's end-to-end ownership of a previous project, from requirements through to production support, as a positive signal. Passing this detail back to the candidate means they walk in knowing precisely which of their own achievements are likely to come up, and can prepare a sharper answer than a generic "talk me through your experience" response would produce.

Step 7: Close the Loop With a Final Quality Check

No candidate goes forward to a client interview without a final sign-off step. Every candidate presented to clients goes through final approval from Dcoded's leadership before submission, as part of maintaining consistency with our certified standards. This isn't a formality — it's the mechanism that stops volume-driven submission from creeping into the process, because a consultant's enthusiasm for a candidate doesn't override a structured final check.

Tips & Best Practices

A few principles run underneath every step above and are worth calling out directly for hiring managers evaluating whether this process will actually reduce their mismatched-fit risk.

• Insist on scorecard-level prep, not CV-matching. If a recruiter can't tell you which competencies your own interview process marks against, they can't be coaching candidates against them.

• Expect engineering fundamentals to be covered explicitly. SOLID, DRY, the Law of Demeter, and the test pyramid aren't trivia — they're standard markers of genuine engineering judgment, and a properly briefed candidate should be ready to discuss all of them, not just the headline skill on their CV.

• Ask whether code review is part of the process, and whether candidates know it's coming. A candidate briefed to expect a live code walkthrough performs differently than one caught off guard by it.

• Look for consultants who can translate technical depth into candidate coaching, even without writing the code themselves. Our own approach is that a recruitment consultant's job isn't necessarily to be the most technical person in the room — it's to understand the how, why, and impact of a candidate's work, alongside their individual drivers and motivations, and to build structured day-to-day training around qualifying that properly.

• Treat "values and alignment" as a real competency, not a soft add-on. Where a client scorecard includes it, a candidate's ability to speak credibly to motivation and fit matters as much as their technical answers.

Troubleshooting: What to Do When the Interview Format Changes Late

Sometimes a client's interview structure shifts after a briefing has already happened — a different interviewer steps in, or the technical focus narrows unexpectedly. The fix is the same discipline that underpins the whole process: go back to source. We confirm interview structure directly before each stage rather than relying on a single scorecard capture at the start of the process, because the conversational-to-technical format and competency weighting can be specific to the individual interviewer running that session. If a candidate reports an unexpected line of questioning after an interview, we feed that back into the briefing process for the next candidate in the pipeline, so the scorecard mapping stays current rather than static.

Key Takeaways

Preparing a candidate for a client technical interview isn't a courtesy call the day before — it's a structured process built around the client's own scorecard, their named competencies, and the realistic shape of their technical conversation, whether that's engineering fundamentals like SOLID and DRY or a live code review of a candidate's own GitHub project. For hiring managers weighing up whether a specialist recruiter can genuinely interrogate technical depth rather than keyword-match a CV, this is the proof mechanism: a documented, repeatable briefing process, closed out with a final sign-off before any candidate reaches your interview room. If your current process is turning up candidates who look right on paper and underperform in the room, the gap usually isn't your interview — it's what happened, or didn't happen, before they walked in.