Training Turk

Excel Expert — Finance

$70–100/hr · Mercor · Hourly, 40 hours a week

You bring advanced Excel expertise from investment banking or private equity to design, solve, and evaluate financial models that train AI systems on professional spreadsheet reasoning.

What you would do

  • Design domain-specific Excel tasks that reflect real financial modeling challenges from your background
  • Build well-structured solutions that demonstrate correct formula logic, model architecture, and financial reasoning
  • Evaluate AI-generated spreadsheets and model outputs for accuracy, internal consistency, and best-practice compliance
  • Author rubrics that define spreadsheet quality standards, including formula audit checks and presentation standards
  • Collaborate asynchronously with research teams to close gaps in AI model spreadsheet capabilities

Who they want

  • Advanced Excel proficiency with complex formulas, pivot tables, dashboards, Power Query, Power Pivot, and VBA automation
  • Professional experience in PE, banking, hedge funds, or research applying financial modeling and valuation techniques
  • Clear evidence of career progression in your finance field
  • Ability to commit reliably to 40 hours per week through end of September, then 20 hours weekly thereafter
  • Prior AI training, model evaluation, or rubric development experience is a strong plus; United States-based applicants only

What the interview asks about

  1. 1.Three-statement model architecture and links

    Flawed connections between income statement, balance sheet, and cash flow silently corrupt valuation outputs, so testing your understanding of proper model structure is crucial.

    For example: “You're designing a three-statement model for a company with significant working capital swings. Show me how you'd link changes in accounts receivable in the balance sheet to the cash flow statement and explain common mistakes.”

  2. 2.LBO model mechanics and leverage dynamics

    LBO models require precise handling of interest expense, principal repayment, and debt schedules; errors here directly distort return calculations that drive investment decisions.

    For example: “An AI model builds an LBO model with linear debt paydown, but the deal has covenant-driven prepayment requirements. Describe what the correct debt schedule looks like and why the AI approach is problematic.”

  3. 3.Formula auditing and error detection in spreadsheets

    Spreadsheet errors often hide until late in analysis, so your ability to systematically audit formulas and catch circular references or incorrect assumptions demonstrates quality discipline.

    For example: “You receive an Excel DCF model with complex circular logic for debt repayment. Describe your step-by-step process for auditing the formulas and identifying whether the circularity is intentional or erroneous.”

  4. 4.Advanced Excel feature selection and execution

    Choosing the right Excel feature (INDEX-MATCH vs. VLOOKUP, Power Query vs. manual links) affects model speed, maintainability, and scalability, so demonstrating judgment here is essential.

    For example: “You need to populate a sensitivity table showing IRR across 50 combinations of assumptions. Would you use Data Table, formulas with INDEX-MATCH, or another approach? Justify your choice.”

  5. 5.Documentation and transparency in financial models

    Models without clear documentation create audit risk and make it impossible for others to verify reasoning, so your standards for sheet notes, formula transparency, and assumption logging matter.

    For example: “You're building a model that a deal team will use for months. Describe the documentation approach you'd use so others can understand key assumptions without contacting you.”

A task you may get

Design and solve a leveraged buyout model for a $500M acquisition including a complete debt schedule, three-statement linkage, and sensitivity tables showing IRR across revenue growth and exit multiple assumptions. Document the key drivers and assumptions.

How to prepare

  • Review 2-3 complex financial models you've built and identify the most common errors or design decisions you'd now handle differently
  • Build a sample three-statement model from scratch using real company data, then stress-test it with extreme scenarios to find breaking points
  • Write detailed documentation for one of your models as if a colleague who has never seen it must understand all linkages and assumptions
  • Practice explaining a sophisticated modeling technique (e.g., circular debt modeling, working capital seasonality, tax effects) as if teaching someone with basic finance knowledge

The facts

Pay
$70–100/hr
Hours
Hourly, 40 hours a week
Where
Remote · United States
Open to
USA
Field
Finance
Posted
9/12/2026
Places left
10

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