Commercial Data Analyst

About Wahoo

At Wahoo, we build training tools that athletes rely on every day, from wheel-off bike trainers and smart bikes to treadmills, bike computers, and sensors. We believe the best products are built by people who use them, and every decision we make is grounded in how real athletes train, compete, and recover. Our teams combine athlete empathy with deep technical rigor, taking a hands-on, build-and-test approach to solving hard problems. We value ownership, clarity, and practical judgment, and we expect every team member to help deliver products and experiences that earn athletes' trust over years of use.

About the Role

The Commercial Data Analyst is the analytical bridge between our Sales and Finance functions, turning Wahoo's rich hardware and software user data into actionable, revenue generating insights. You will use that data to sharpen how we target and sell directly to customers. Where possible you will prepare and share learnings with B2B partners to drive sell-through and Wahoo’s market share.

You will approach every commercial action as a hypothesis: design it to be measured, learn from the result, and build a growing body of knowledge about what drives our customers and our business. This is a hands-on technical role for someone early in their career who wants to apply real data science skills to profit generating actions.

What You'll Do

• Build and evaluate customer segmentation and propensity models using regression, clustering, and ensemble methods. You will leverage Wahoo’s device registration, usage, subscription, and other demographic data to drive results so Sales and Finance can act with confidence.
• Translate model results into go-to-market approaches: target audiences and campaigns for D2C, and customer and market insights that support B2B partners' assortment, sell-through, and co-marketing plans.
• Assist in designing, running, and evaluating pricing and other go-to-market strategies with Sales and Finance, measuring what drives conversion, upgrades, retention, elasticity, and profitability.
• Document methods, assumptions, and outcomes so learnings compound over time, including financial analysis to ensure profitability metrics are achieved. This includes participating in reviews of analytical approaches with stakeholders.
• Analyze purchase and usage data to identify leading indicators of future high-value customers, to drive targeted actions.
• Identify and maintain key customer value metrics, such as lifetime value, repeat purchase rate, etc. to identify our highest-value customers and where to invest in go-to-market to find more like them.
• Ensure data quality, robustness, and consistency across sources, and investigate anomalies and metric discrepancies when numbers don't line up.
• Automate recurring data validation and reporting workflows in SQL and Python so stakeholders can rely on consistent, reproducible numbers. Build and maintain dashboards on funnel performance, cohort health, and campaign results.

Scope & Decision Authority

• Owns defined analyses, reports, and outcomes within assigned go-to-market strategies.
• Continuously evaluate and maintain data driven, short-term sales opportunities across key markets.
• Makes local analytical and methodological decisions within established frameworks, with guidance on strategy and prioritization.

What We're Looking For

• Bachelor's degree in Data Science, Statistics, Economics, or similar. Curious, hypothesis-driven, and motivated by learning what works.
• Understanding of core commercial concepts and the ability to connect analysis to its impact on revenue, gross margin, and profitability
• 2-4 years of experience in data analysis, analytics, or data science, ideally in a commercial or consumer products setting.
• Proficiency in SQL and working large datasets, as well as proficiency in Python for data analysis and modeling
• Familiarity with modern cloud data stacks (AWS, Redshift, dbt) is a plus.
• Strong background in statistics, including hypothesis testing, confidence intervals, and experimental design for A/B tests.
• Understanding of modeling concepts such as regression, feature selection, clustering, and ensemble methods.
• Comfort using AI-assisted tools to accelerate analysis, execution, and reporting while holding the output to a high-quality standard.
• Working knowledge of customer analytics concepts, including segmentation, lifetime value, cohort retention, and how they drive the P&L.
• Experience with BI tools such as Power BI or Tableau is a plus
• Strong analytical and problem-solving skills, with the ability to work through ambiguous questions and communicate findings clearly to non-technical audiences.
• Interest in fitness and endurance sports is a plus.