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Mariona Baneras

Sr. Data Scientist

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About Mariona

Mariona is a physicist and data scientist with a research background and more than eight years of industry experience. She has worked with various companies ranging from digital startups to global insurance firms. Throughout her professional journey, Mariona has helped companies achieve success in their pricing and revenue management endeavors by providing impactful data-driven solutions and rigorous analysis.

Early in her career, she honed her consulting skills in corporate strategy and intrapreneurship. Mariona has developed a strategic mindset that allows her to deliver tailored solutions to clients’ unique challenges.

Mariona possesses several core strengths:

  • Advanced analytics expertise: Mariona leverages statistical and machine learning techniques to optimize pricing and promotions, forecast demand, and empower decision-makers to drive business growth.

  • Data visualization proficiency: Mariona has a passion for designing intuitive, visually stimulating data visualization solutions that drive adoption and help discover key insights.

  • Technical know-how: Mariona has a strong foundation in development and project management, enabling her to automate critical processes and seamlessly integrate systems for enhanced operational efficiency.

Recent Articles

Pricing Intelligence Engine

Rebuilding Pricing and Promotion Analytics for a Global Data-Storage OEM

April 21, 2026

A Fortune 500 global data-storage OEM was bleeding margin in its $200M U.S. B2C hard-drive business. One flagship family had taken a substantial net-pricing hit year-over-year, and roughly 45% of historical promos were returning only 0 to 20% ROI. Revology rebuilt the pricing and promotion analytics from the ground up using causal Double Machine Learning, a retailer-math ROI model, and a three-archetype segmentation framework. The target: $3M to $6M of incremental EBITDA (a 10x to 20x return on the engagement) within 12 months.

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Project APEX Pricing Power

Unlocking Pricing Power for a Global Pharmaceutical Manufacturer in Emerging Markets

April 21, 2026

A Fortune 500 global pharmaceutical manufacturer was making emerging-market pricing decisions by feel. We built a repeatable Pricing Quick Wins engine across four pilot markets, grounded in causal elasticity modeling, automated competitive equivalence mapping, and price-pack architecture and inflation-aware simulators. The pilots identified around $8M of median revenue opportunity, with a best-case of ~$12M. Local teams now own the engine and can repeat the analysis annually as inflation and the competitive set shift.

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Automated RGM Engine

Operationalizing Revenue Growth Management Analytics for a Leading Plant-Based Creamer Brand

April 21, 2026

A leading plant-based creamer brand wanted real visibility into more than $13 million of annual trade spend and a credible way to forecast promo ROI before writing checks. We built the Revenue Growth Management analytics engine for them in Python and Power BI, running on their existing stack. The team now refreshes pricing, promo, and revenue/gross profit performance deep dive models in 10 to 20 minutes and catches variance the old process missed by weeks.

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