Senior Data Analyst, Finance

Scale Media builds science-backed wellness brands and is investing in data and AI to improve product development and brand growth. The Senior Data Analyst, Finance will own financial forecasting, predictive modeling, reporting, reconciliation, and financial data integrity while partnering with Finance and executive leadership.

Responsibilities:

• Financial Forecasts: Build and own forecasts for revenue, orders, subscription renewals, and cash by brand and channel
• Scenario Planning: Develop scenario and sensitivity models that help Finance plan budgets
• Forecast Accuracy: Track accuracy over time and refine forecasts as the business changes
• Customer & SKU-Level Models: Build, validate, and maintain predictive models, such as subscription renewal and cancellation likelihood, refund and chargeback risk, and SKU-level demand, using statistical and machine learning methods (e.g., logistic regression, survival analysis, gradient boosting)
• Sharper Forecasts: Feed model outputs into revenue and cash forecasts so projections reflect who is likely to renew, cancel, or refund, not just historical trends
• Model Health: Monitor model performance over time and retrain as customer behavior shifts
• P&L Reporting: Own reporting on revenue, gross vs. net sales, discounts, refunds, COGS, and contribution margin by brand, channel, and product
• Unit Economics: Own contribution margin, payback, and profitability reporting by brand, channel, and product
• Variance Analysis: Produce budget-vs-actual analysis that explains what moved and why
• Reconciliation Ownership: Own reconciliation across payment processors (Stripe, Shopify Payments, Amazon), bank deposits, our internal database, and accounting (QuickBooks) to ensure every dollar is accounted for
• Root Cause Leadership: Lead investigations of revenue discrepancies and drive fixes at the source with our Data Engineer
• Single Source of Truth: Define and document the business logic behind financial metrics for the whole company
• Data Preparation: Use dbt or Python to build the financial datasets and models behind forecasting and reporting
• Business Logic Application: Write efficient SQL in Snowflake that reflects our financial business rules
• Dashboarding: Design, build, and maintain high-impact dashboards in Sigma for Finance and executive leadership
• Finance Partnership: Present forecasts and model findings to Finance and executive leadership, including the assumptions and risks behind them
• Raise the Bar: Review other analysts' work and mentor them on financial data quality and modeling

Requirements:

• • Experience Level: 5+ years of data or financial analytics experience, with meaningful time in DTC, subscription, or consumer businesses. (We are looking for a senior, hands-on individual contributor, not a people manager.)
• • Forecasting & Predictive Modeling: Hands-on experience with both: building financial forecasts (e.g., time-series, regression) and predictive models (e.g., logistic regression, survival analysis, gradient boosting) that leadership used to make decisions. Both are required
• • Financial Acumen: Strong grasp of revenue recognition, COGS, gross vs. net sales, contribution margin, and cash flow
• • Industry: Proven experience in DTC E-commerce is required. You must be comfortable with Shopify, Amazon Seller Central, and Stripe data
• • Warehousing & BI: Expert SQL in Snowflake, strong Python (pandas, scikit-learn, statsmodels, Prophet, or similar), and proficiency with Sigma. We're open to candidates experienced with Tableau, Looker, or Power BI who can adapt quickly
• • Spreadsheets: Advanced Excel or Google Sheets skills
• • AI Tools: Fluency with Claude Code (or comparable AI coding tools) for data manipulation and analytics
• • ELT Familiarity: Awareness of how ELT tools (like Hevo, Fivetran, Airbyte, Azure, etc.) function, so you can communicate effectively with our Data Engineer
• • You have built forecasts leadership actually planned around, and you can explain why a forecast missed and what you changed
• • You know when a time-series forecast is enough and when a customer-level predictive model will do better
• • You understand how a Shopify order becomes a processor payout and then a bank deposit, and where fees, refunds, and chargebacks create gaps
• • You present forecasts honestly, with the assumptions and risks in plain view
• • You have the patience and precision to trace a single transaction through the entire data lifecycle when the numbers don't add up
• • Familiarity with QuickBooks report structures and month-end close is a plus