$100–150/hr · Mercor · Full time, 40 hours a week
Senior finance specialist training frontier AI models to reason accurately about complex mergers, valuations, and financial decisions.
What you would do
- Evaluate AI-generated analyses of mergers, valuations, and corporate transactions for analytical accuracy and alignment with market practice
- Assess whether AI reasoning about financial scenarios reflects real-world constraints and professional judgment standards
- Design realistic test cases and benchmark problems grounded in actual deals and financial methodologies
- Create golden-standard solutions and evaluation criteria that define high-quality financial reasoning for AI systems
- Collaborate with research teams to translate implicit financial knowledge into explicit, measurable benchmarks
Who they want
- 5+ years of substantive professional finance experience at investment banks, asset managers, private equity firms, or equivalent top-tier institutions
- Senior-level expertise demonstrated through progression to VP, Director, Principal, Managing Director, or portfolio manager positions
- Genuine specialization in core finance discipline such as M&A, corporate finance, investment banking, private credit, or quantitative risk
- Advanced degree in finance, economics, accounting, or quantitative field; professional credentials like CFA, CPA, or FRM strongly preferred
- Hands-on experience using AI language models in professional work with judgment to assess quality of AI-generated analysis
Main skills
What the interview asks about
1.Valuation methodology selection
AI systems must choose correct valuation approaches for different scenarios; your expertise determines whether models apply appropriate methodologies and assumptions.
For example: “An AI system was asked to value a private software company for an acquisition. It applied a 4-year DCF with 15% discount rate. What would you scrutinize about this approach?”
2.Deal structure reasoning
Complex financial decisions rest on understanding how deal structures create value or risk; evaluating AI reasoning here requires deep M&A experience.
For example: “Given an AI recommendation on earnout structure for a $150M tech acquisition, what financial risks or incentive misalignments would you assess?”
3.Market dynamics understanding
AI models must reflect how actual markets behave and how professionals interpret signals; spotting unrealistic assumptions is critical to quality.
For example: “An AI analysis claims that multiples compression in this sector is temporary and justifies a premium valuation. How would you evaluate the reasoning?”
4.Financial assumption realism
Many AI errors arise from unrealistic or incomplete assumptions about market conditions, competitor behavior, or execution risk that real professionals would flag.
For example: “An AI model forecasts 25% revenue growth for 8 years for a mature industrial company. What questions would make you reject or revise this assumption?”
5.Benchmark standard setting
Creating evaluation benchmarks that separate truly competent AI reasoning from plausible-sounding errors requires mastery of where AI typically fails in finance.
For example: “Design a single-problem benchmark that would reveal whether an AI system genuinely understands sensitivity analysis in M&A pricing versus just citing correct terminology.”
A task you may get
Evaluate an AI analysis of a $500M acquisition for valuation accuracy, deal structure logic, and financial realism. Identify errors and explain what missed real-world practice.
How to prepare
- Review recent M&A activity and public financial analyses to understand current market dynamics, common methodologies, and valuation trends in your specialty
- Prepare concrete examples from your own deals where initial analyses required revision due to market realism, execution risk, or structural considerations overlooked initially
- Study how experienced finance teams frame complex decisions and articulate judgment about feasibility, value creation, and risk management
- Practice explaining financial reasoning clearly to non-finance audiences, focusing on building intuition about market dynamics and decision-making under uncertainty
The facts
- Pay
- $100–150/hr
- Hours
- Full time, 40 hours a week
- Where
- Hybrid · Bay Area, CA
- Open to
- USA
- Field
- Finance
- Posted
- 8/12/2026
- Places left
- 5
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