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Ōura is seeking a Forecast Analyst – Demand Planning to strengthen forecasting capabilities through advanced analytics, improved data integration, and scalable demand methodologies. This senior individual contributor role serves as the analytical engine behind demand planning, translating business signals into accurate forecasts, building statistical baselines, and advancing the organization toward machine learning-enabled forecasting.
Key responsibilities include developing and operationalizing forecasting methodologies that improve accuracy and responsiveness across planning processes. You will build and refine statistical baseline forecasts using historical demand, seasonality, promotions, product transitions, and channel mix data. The role involves driving Ōura's evolution toward ML-enabled forecasting by identifying high-value use cases, testing approaches, and translating outputs into practical planning decisions. You'll evaluate forecast performance through error diagnostics and bias analysis, turning findings into clear improvement recommendations.
Data integration work includes partnering with Data and Systems teams to improve the structure and reliability of demand planning inputs. You'll connect data across shipments, POS, inventory, promotions, and product lifecycle events to create a more complete demand signal. The role focuses on reducing manual work and improving planning speed through better tooling and automation.
Decision support involves building scenario models, dashboards, and analytical tools that help the business understand demand risk and operational implications. You'll support monthly, quarterly, and long-range planning cycles with decision-ready analysis, partnering closely with Demand Planning and Supply Chain teams. Complex analytical findings must be translated into concise narratives for both technical and non-technical stakeholders.
Required qualifications include 6+ years in forecasting, demand planning, analytics, data science, or operations research. You need strong statistical forecasting experience in business environments, advanced SQL skills, and proficiency in Python, R, or similar analytical programming languages. Experience with large, messy datasets, model development and evaluation, and planning processes in consumer products, hardware, or retail is essential. You must demonstrate the ability to translate analytical work into business recommendations and partner effectively across functions.
Nice-to-have qualifications include experience with machine learning for time-series forecasting, planning platforms and BI tools, consumer electronics or wearables background, and experience scaling forecasting processes in high-growth companies.
