Ifooddecisionsciences AI Research Scientist interview typically runs 3 rounds: RH, case técnico com gerentes da área, e entrevista com a liderança. O processo levou cerca de 1 mês e foi descrito como pouco claro e inconsistente.
$123K
Avg. Base Comp
$169K
Avg. Total Comp
3-4
Typical Rounds
2-4 weeks
Process Length
Our candidates report a process that feels less like a structured evaluation and more like a moving target. The strongest signal here is not a hidden technical bar, but internal inconsistency: one candidate described expectations that seemed to shift from one conversation to the next, with little alignment between the people involved. That kind of drift usually means the team is still defining the role, and candidates who do best are the ones who can quickly infer what matters from vague prompts and still anchor their answers in concrete outcomes.
A recurring theme is the lack of context around the technical case. The candidate said the objectives were never made clear, which made it hard to know what success looked like. We’ve also seen the same pattern in the manager conversation: questions were broad, generic, and only lightly tied to the actual work, with the most specific prompt being how success is measured. That tells us this company may care more about whether you can impose structure on ambiguity than about any single polished framework.
The final impression matters here, and not just for technical reasons. The candidate noted that leadership asked questions without context and even veered into personal territory in a way that felt insensitive. That suggests the interviewers may be testing judgment and presence as much as research depth, but without much finesse. For our candidates, the real make-or-break factor is showing they can stay composed and precise when the process itself is messy.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Ifooddecisionsciences process.
Fui abordado via LinkedIn para participar do processo seletivo, e logo de cara a sensação foi de pouca clareza sobre o que a vaga realmente exigia. O processo teve uma conversa com RH, depois um case técnico com gerentes da área e, por fim, uma entrevista com a liderança. O problema é que, ao longo das etapas, as expectativas pareciam mudar ou pelo menos não estavam bem alinhadas entre as pessoas envolvidas. Em vez de sair com uma visão mais nítida do cargo, eu terminei com mais dúvidas do que no início.
O case técnico foi especialmente frustrante porque não deixaram objetivos de avaliação claros, então ficou difícil entender o que exatamente estavam buscando. Na entrevista com os gerentes, as perguntas foram bem genéricas e sem muito contexto, o que deixou a conversa pouco produtiva. A pergunta mais concreta que me fizeram foi sobre como eu media sucesso, mas mesmo essa veio de forma bastante aberta, sem conexão forte com o dia a dia da posição. Já na etapa final com a liderança, além de perguntas sem contexto, também entraram em temas pessoais que não tinham relação com a vaga. O ponto mais negativo foi a forma como um assunto pessoal e delicado foi tratado, de maneira bem insensível e superficial. No fim, a impressão geral foi de um processo desorganizado, com pouca empatia e pouca consistência interna sobre o que esperavam do candidato. Não recebi oferta.
Prep tip from this candidate
Vale chegar preparado para responder com exemplos concretos de como você mede sucesso no seu trabalho, porque essa foi a única pergunta mais específica que apareceu. Também ajuda estar pronto para um case técnico com critérios pouco claros e para perguntas genéricas na conversa com gestores e liderança.
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Topics based on recent interview experiences.
Featured question at Ifooddecisionsciences
Search for a value in log(n) over a sorted array that has been shifted.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Experiment Validity | |
| Merge Sorted Lists | |
| Bagging vs Boosting | |
| First to Six | |
| Scrambled Tickets | |
| 500 Cards | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| String Shift | |
| Compute Deviation | |
| Weekly Aggregation | |
| Button AB Test | |
| Raining in Seattle | |
| Lazy Raters | |
| Nearest Common Ancestor | |
| Job Recommendation | |
| Friendship Timeline | |
| Impression Reach | |
| Bank Fraud Model | |
| Jars and Coins | |
| RMS Error | |
| N-gram Dictionary | |
| Variable Error | |
| Network Experiment Design | |
| Prime to N | |
| Radix Addition | |
| Swipe Precision | |
| Minimum Change |
Synthesized from candidate reports. Individual experiences may vary.
The candidate was first approached via LinkedIn to join the hiring process. At this stage, the role itself was still not very clearly defined, and the candidate felt there was limited clarity about what the position actually required.
The process began with a conversation with HR. This stage appears to have been an introductory screening, but the candidate noted that expectations were not well aligned across the process and the role requirements were still somewhat unclear.
The candidate completed a technical case with managers from the team. The case was frustrating because the evaluation criteria were not made clear, and the questions felt generic and lacking context, making it difficult to understand what the interviewers were looking for.
The final stage was an interview with leadership. In addition to broad, context-light questions, the conversation also included personal topics that were not related to the role, and the candidate felt one sensitive personal subject was handled insensitively.
