Job description
About Scale
For the past 12 years, Scale Media has been building and scaling science-backed wellness brands. Our portfolio of brands, including Live Conscious, 1MD, and Essential Elements, has grown to 9 figures in revenue while operating profitably with no external funding.
Our team is made up of operators, marketers, and builders focused on products consumers actually trust. We're investing heavily in data and AI to sharpen how we develop products and scale brands.
We believe the best decisions come from data, curiosity, and continuous learning. We value high ownership, candid feedback, and thoughtful execution. As a fully remote company, we lead with clear communication, accountability, and trust, and we give people real room to grow and make an impact.
Scale Media has been recognized as one of Forbes' Best Startup Employers, a Great Place to Work, an Inc. Best Workplace, and an Inc. 5000 company.
If you take ownership, think for yourself, and like solving hard problems alongside people who do the same, you'll fit right in.
About the Role
We are looking for a Senior Data Analyst who is far more than a report builder. You are a "Data Detective" who understands the heartbeat of an e-commerce P&L, and where it's headed next.
You will own the truth behind our financial numbers and how we forecast them. We need a Financial Modeling Master who can build the forecasts our Finance team plans around, plus the predictive models that make those forecasts sharper, all grounded in rigorously reconciled data.
Additionally, you will act as a critical partner to our Finance team and leadership, bridging the gap between raw transaction data, payment processors, and the financial plan.
What You'll Do
1. Forecasting
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.
2. Predictive Modeling
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.
3. Financial Reporting & Unit Economics
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.
4. Financial Data Integrity & Reconciliation
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.
5. Data Transformation & Visualization
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.
6. Leadership & Collaboration
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.
What We're Looking For
The Essentials (Experience & Tech Stack)
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 accounting principles, and familiarity with compiling financial statements and cash flows.
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.
The "Financial Modeling Master" Skill Set
You have a proven track record of designing forecasts that executive leadership relies on for strategic planning, along with the ability to articulate variance drivers and continuously calibrate model accuracy.
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.
Familiarity with QuickBooks report structures and month-end close is a plus.
The Financial Mindset
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.
What We Offer
Salary: $100,000 - $125,000 depending on experience
Excellent Medical, Dental, Vision and Life insurance
Fully remote, full-time position
Monthly WFH stipend
Generous Paid Time Off program
Discounted Products
An amazing team to work alongside
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Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.