Lead, Analytics, Data & Strategic Finance

About the Role

Braintrust is seeking a Lead, Analytics, Data & Strategic Finance to build the analytical foundation for GRIN's evolution from a SaaS platform into a creator commerce marketplace. This pivotal role blends advanced analytics with financial planning, requiring expertise in predictive modeling, statistical analysis of large datasets, and translating findings into actionable financial and product strategies. You will dedicate approximately 60% of your time to analytics and 40% to core FP&A support.

As GRIN expands into marketplace dynamics, including creator payments, platform fees, and GMV-driven revenue models, you will be instrumental in developing key metrics frameworks, robust forecasting methodologies, and essential analytical tooling. The remaining 40% of your responsibilities will focus on the financial planning cycle, including budget vs. forecast analysis, board reporting inputs, and ad hoc modeling.

Analytics (60%)

Customer & Marketplace Intelligence

• Build and maintain predictive models (churn, expansion, conversion) using behavioral, financial, and engagement data across both brand and creator populations.
• Develop health scoring systems to quantify account-level risk across ARR portfolio and emerging marketplace revenue streams, segmented by engagement intensity, revenue scale, product adoption, and transaction activity.
• Design cohort analyses to track customer and creator lifetime value trajectories, identify retention inflection points, and surface growth opportunities across subscription and marketplace revenue.
• Build marketplace economics models: GMV forecasting, platform fee optimization, creator payment velocity, and supply/demand balancing across the creator network.

Statistical Analysis & Methodology

• Apply statistical process control (SPC) methods to differentiate signal from noise in financial and operational metrics, establishing statistically meaningful control limits.
• Implement driver-based forecasting frameworks that identify operational levers with the highest predictive power for revenue outcomes.
• Run significance testing, regression analysis, and feature importance modeling to quantify customer behaviors that predict renewal, contraction, and expansion.
• Build distribution analyses (percentile breakdowns, standard deviation mapping) across key metrics to establish data-driven thresholds for customer risk tiering.

Analytics Engineering & Tooling

• Develop automated analytical workflows and interactive dashboards for insightful, manual-intervention-free reporting.
• Work with production datasets from NetSuite, Salesforce, Stripe, and internal product databases via CSV exports or direct database connections.
• Write clean, reproducible analytical code in Python, SQL, or R, utilizing version-controlled notebooks or scripts.
• Evaluate, implement, and maintain BI and analytics tooling that scales with the business, transitioning from pure SaaS metrics to hybrid subscription and marketplace KPIs.
• Document methodologies, assumptions, and model performance to ensure analyses are auditable and repeatable.

Strategic Finance (40%)

• Support quarterly forecasting and annual budgeting processes with analytical inputs: trend analyses, scenario modeling, and sensitivity testing across both subscription and marketplace revenue lines.
• Prepare variance analysis connecting financial outcomes to underlying operational drivers.
• Contribute to board reporting packages with data visualizations and analytical summaries, translating complex analyses into executive-ready narratives.
• Build and maintain financial models in Google Sheets for ad hoc strategic questions from leadership.
• Partner with Accounting on data reconciliation and integrity checks across financial systems, including emerging marketplace transaction flows.

Qualifications

Required

• 7+ years of experience in data science, analytics, or quantitative finance roles, preferably in B2B SaaS or marketplace businesses.
• Strong proficiency in SQL and at least one programming language for statistical analysis (Python or R).
• Demonstrated experience building predictive models (classification, regression, clustering) on real business datasets.
• Solid understanding of SaaS and marketplace metrics: ARR, NRR, churn, GMV, take rate, CAC, LTV, and cohort analysis.
• Experience working with messy, real-world data from CRMs, billing systems, payment platforms, and product databases.
• Strong financial literacy: ability to read a P&L, understand revenue recognition basics, and contextualize analytical findings within a financial planning framework.
• Ability to communicate statistical findings to non-technical stakeholders clearly and without jargon.

Preferred

• Experience with statistical process control, time-series forecasting, or Bayesian methods.
• Prior experience in marketplace or platform businesses with two-sided economics.
• Background in FP&A, management accounting, or financial modeling alongside data science skills.
• Experience with NetSuite, Salesforce, Databricks, and Stripe data architectures.
• Familiarity with driver-based planning or rolling forecast methodologies.
• Experience building analytics for payment flows, GMV reporting, or creator/gig economy platforms.

Key skills/competency

• Predictive Modeling
• Statistical Analysis
• Financial Planning & Analysis (FP&A)
• Marketplace Economics
• SQL
• Python/R
• SaaS Metrics
• Data Visualization
• Forecasting
• Data Engineering