Sales Compensation Analyst (Financial Modeling & Planning)

S&P Global•Virtual, NY

11d

About The Position

The Sales Compensation Analyst – Financial Modelling and Planning is a quantitative specialist on S&P Global Market Intelligence’s global Sales Compensation team. The role designs and runs simulation and scenario models to govern incentive spending across the global sales force. It forecasts commission accruals, stress-tests plan structures, and delivers predictive analytics to help align compensation spending with divisional financial targets. The analyst models potential outcomes before decisions are made and measures actual results afterward, grounding plan design in evidence. The position builds financial forecasting models and automated analytics, evaluates compensation-plan effectiveness and risk, and translates modelling outputs into executive dashboards, presentations, and recommendations for Commercial and Finance leadership. It also supports scenario planning through reusable tools and works with Data Architecture teams to automate and maintain reliable data flows into modelling workspaces. The role requires at least five years of relevant experience, advanced financial modelling and automation capabilities, Power BI development expertise, and the ability to communicate complex analyses to senior leaders. It is limited to people with indefinite right to work in the United States. S&P Global encourages people returning to work after time away for caring responsibilities to apply through its Restart initiative.

Requirements

At least 5 years of experience in financial modelling, quantitative compensation analysis, corporate FP&A, or incentive strategy within a multinational organisation.
Proven experience building nested-logic models, dynamic-array models, and Monte Carlo or multi-variable sensitivity simulations for large-scale compensation expense forecasting and risk quantification.
Hands-on experience using Python (Pandas/NumPy) or Alteryx for data manipulation and programmatic financial modelling, combined with advanced SQL querying capability across large, multi-table datasets.
Demonstrated experience building sophisticated Power BI data-visualisation models using advanced DAX measures and star-schema architectures for executive-level reporting.
Demonstrated ability to synthesize complex quantitative modelling outputs—including forecast results, stress-test findings, and attainment analyses—into clear, structured recommendations for senior Finance and Commercial executives.
Indefinite right to work in the United States.

Nice To Haves

Experience using corporate financial planning tools such as Adaptive Insights, Anaplan, or Workday Adaptive in a compensation or revenue planning context.
Experience applying Monte Carlo simulation or probabilistic modelling techniques to sales compensation forecasting and tail-risk exposure quantification.
Experience constructing attainment distribution analyses and quota-cliff assessments to identify plan-design distortions across global sales territories.
Experience analysing the correlation between compensation expense ratios, including CAC and COC ratios, and product-line profitability or net new ACV in a B2B SaaS or subscription-based sales environment.
Experience building and maintaining version-controlled financial modelling repositories with longitudinal baseline data for multi-year compensation plan performance comparisons.
Experience partnering with Data Architecture or Data Engineering teams to design automated data-ingestion pipelines from enterprise data warehouses into financial modelling workspaces.
Experience using Python SciPy, Statsmodels, or R for advanced statistical modelling, including regression analysis and distribution fitting, in a sales compensation or FP&A context.

Responsibilities

Build and execute dynamic financial forecasting models using Python (NumPy/Pandas) and advanced Excel/Power Query to simulate compensation expense, model global commission accruals, and support FP&A alignment and month-end payout estimates.
Cross-reference run-rate performance with historical seasonality trends to support forecasting and payout estimates.
Stress-test proposed compensation structures and conduct sensitivity analyses involving accelerator tiers, commission caps, milestone bonuses, and quota changes.
Forecast risk boundaries and tail-risk exposure, and construct statistical attainment distributions to identify plan distortions, windfall payouts, and quota cliffs across global sales territories.
Design and calculate plan-effectiveness metrics, including analysis of compensation expense in relation to product-line profitability and net new ACV.
Evaluate the ROI and behavioural impact of non-standard incentive programs, including SPIFFs, strategic account bonuses, and multi-year contract multipliers.
Develop and maintain automated Power BI executive dashboards tracking commission expense ratios, average realisation rates, CAC/COC ratios, and quota attainment curves.
Translate quantitative modelling outputs into clear, narrative-driven executive presentations and briefing decks for Commercial and Finance leadership.
Build reusable, script-based scenario calculators using Python, SQL, and Alteryx to support rapid simulation of mid-year crediting and plan adjustments.
Maintain version-controlled mathematical repositories for longitudinal baseline comparisons across fiscal cycles.
Partner with Data Architecture teams to automate data ingestion from enterprise data warehouses into financial modelling workspaces and ensure pipeline integrity, data freshness, and modelling accuracy.