Senior Sales Compensation Modeller & Forecasting Lead

S&P Global; United States; Remote; USD 71,000 - 125,000; Full time

Benefits offered by this job

Health & Wellness

Flexible Downtime

Continuous Learning

Invest in Your Future

Family Friendly Perks

Beyond the Basics

Job summary

S&P Global is seeking a Sales Compensation Analyst to design and run models that govern global incentive spend. You will stress-test plan structures, forecast accruals, and deliver predictive analytics to align compensation with financial targets.

The role blends Python, advanced Excel, and Power BI to create dashboards and scenario tools for FP&A and Commercial leadership worldwide.

Qualifications

• 5+ years in financial modelling or FP&A within a multinational company.
• Advanced modelling including Monte Carlo/sensitivity analyses.
• Python (Pandas/NumPy) or Alteryx for data manipulation and modelling.
• Power BI with DAX for executive-level reporting.
• Ability to communicate complex results to senior leadership.

Responsibilities

• Build and run dynamic forecasting models for sales compensation.
• Stress-test compensation structures and forecast risk boundaries.
• Design plan effectiveness metrics linking expense and profitability.
• Develop Power BI dashboards tracking comp expenses and attainment curves.
• Create scenario calculators with Python/SQL/Alteryx for mid-year adjustments.
• Collaborate with Data Architecture to automate data ingestion for modelling.

Skills

Financial modelling

Quantitative analysis

Forecasting

Scenario modelling

Strategic communication

Tools

Python

SQL

Alteryx

Power BI

Excel/Power Query

Job description

S&P Global is seeking a Sales Compensation Analyst to design and run models that govern global incentive spend. You will stress-test plan structures, forecast accruals, and deliver predictive analytics to align compensation with financial targets.

The role blends Python, advanced Excel, and Power BI to create dashboards and scenario tools for FP&A and Commercial leadership worldwide.