S&P Global AI Research Scientist interview typically runs 3 rounds: online aptitude test, virtual technical interview, and in-person HR/technical round. The process is notably finance-heavy, emphasizing investment and accounting knowledge over coding.
$130K
Avg. Base Comp
$230K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that S&P Global's AI Research Scientist process is far more finance-weighted than the job title implies. The standout pattern from what we've seen is how quickly the technical interview pivots away from modeling or ML methodology and into investment and accounting fundamentals — one candidate was walked through cash flow analysis, share buybacks, stock splits, and how bonus issues appear in financial statements. That's not a warmup question; that is the interview.
What makes this tricky is the deceptive simplicity of the questions. The wording is plain, but the expected depth is not. Interviewers here seem to probe whether you can reason about financial data the way an analyst would, not just whether you can describe an algorithm. The real signal they're looking for is whether your AI work is grounded in genuine financial domain knowledge — candidates who can only speak to the technical side without connecting it to how business events flow through the numbers appear to struggle here.
The final in-person stage adds a communication and culture-fit dimension, which suggests S&P Global is also evaluating whether you can translate complex ideas clearly to non-technical stakeholders. Taken together, the process rewards candidates who sit at the intersection of quantitative rigor and financial literacy — not just strong AI researchers who happen to be applying to a finance firm.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the S&P Global process.
The selection process was fairly structured and had three stages. It started with an online test that was meant to check aptitude, technical knowledge, and problem-solving ability. After that, I moved on to a one-on-one technical interview over an online meeting, where the discussion was less about coding and more about practical domain knowledge. The final stage was an in-person face-to-face round that combined HR and technical questions and seemed aimed at judging both communication and overall fit for the role.
What stood out most was how finance-heavy the technical interview was for an AI Research Scientist role. I was first asked to introduce myself, and then the conversation shifted into investment and accounting fundamentals. I had to explain what fundamentals I would look at before investing in a company, go into cash flow in depth, and describe how share buybacks, stock splits, and bonus issues show up in financial statements. The questions were straightforward in wording but expected a solid understanding of financial concepts rather than surface-level definitions. Overall, the process felt more practical than theoretical, and the final outcome was a rejection.
Prep tip from this candidate
Be ready to explain core financial statements clearly, especially cash flow and how corporate actions like buybacks, stock splits, and bonus issues are reflected in them. It would also help to practice answering why you would invest in a company using concrete fundamentals rather than generic growth metrics.
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Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
The process begins with an online test covering aptitude, technical knowledge, and problem-solving ability. This stage is designed to screen for general reasoning and baseline domain understanding before advancing candidates to later rounds.
Candidates complete a one-on-one technical interview conducted over an online meeting. Despite the AI Research Scientist title, the discussion is heavily finance-focused, covering investment fundamentals, cash flow analysis, and how corporate actions such as share buybacks, stock splits, and bonus issues appear in financial statements.
The final stage is an in-person face-to-face round that blends HR and technical questions. This round assesses communication skills, cultural fit, and the candidate's ability to discuss both domain knowledge and behavioral topics in a live setting.