The Role

A Principal Product Manager at HelloFresh is a product leader. They work backwards from the most ambiguous and complex topics across Tribes & Alliances to establish a long term business & product plan, applying appropriate best practices while understanding the appropriate value, usability, feasibility, and business viability risks. 

As a Principal Product Manager - Algorithmic Optimization & Science  in Procurement & Assortment teams, you will lead the strategy and execution of advanced algorithms designed to improve personalization, menu planning, and forecasting across our product suite. You’ll be at the forefront of designing and optimizing algorithms that power our recommendation engines, dynamic menu optimizations, and accurate demand forecasting models, all while experimenting and rapidly iterating on the algorithms to ensure that we continuously improve based on data and user feedback. This is a strategic role given the importance of creating a better assortment and more efficient forecasting and menu planning, and presents a powerful opportunity to redefine the data and technology solutions that our physical product teams and operations teams use across 18 countries and 8 brands at HelloFresh Group.

What you will do

1. Lead Product Strategy: Define and drive the product roadmap for personalization, menu planning, and forecasting algorithms, ensuring they align with company goals and customer needs.
2. Algorithm Optimization & Innovation: Work closely with data scientists and engineers to enhance machine learning models, recommendation algorithms, and optimization algorithms for dynamic menu planning and personalized experiences.
3. Cross-Functional Collaboration: Partner with physical product and operations teams to translate technical algorithmic strategies into business outcomes and user benefits.
4. Experimentation Framework: Develop and lead A/B testing, multi-armed bandit experiments, and other experimentation strategies to validate and improve personalization algorithms and menu planning optimizations. Use experimental results to continuously iterate on and refine algorithmic models.
5. Customer-Centric Innovation: Use data-driven insights and customer feedback to improve personalization algorithms, ensuring that the product delivers personalized content, recommendations, and menu options that delight customers.
6. Data Integration & Scalability: Oversee the integration of diverse data sources (e.g., customer preferences, inventory data, seasonal trends, historical demand) to optimize personalization and menu planning algorithms. Ensure the scalability and reliability of data pipelines.
7. Metrics & KPIs: Define and track kpi trees for algorithm performance (e.g., personalization accuracy, recommendation relevance, menu optimization success, forecasting precision), and measure the impact of experimentation on product features.
8. Market & Competitive Analysis: Stay updated on industry trends in machine learning, optimization, and experimentation, identifying new tools, methodologies, or best practices that can enhance algorithm performance.
9. Leadership & Mentorship: Provide mentorship to junior product managers and work collaboratively across teams to build a culture of data-driven decision-making

What you’ll bring

• Experience with building and leading successful products with a focus on data science, machine learning and operations research algorithms.
• Knowledge of personalization algorithms (e.g., collaborative filtering, content-based recommendation systems) and optimization models for resource allocation, menu planning, or demand forecasting. Experience with A/B testing, multi-armed bandit experiments, and experimentation frameworks is required.
• Strong ability to connect algorithmic solutions to tangible business outcomes. Understand the needs of both customers and stakeholders, ensuring that algorithm improvements support strategic company goals.
• Proven track record of leading cross-functional teams to deliver impactful, data-driven products. Experience influencing without direct authority and aligning teams around a shared vision.
• Strong quantitative background, with the ability to analyze and interpret complex data, and apply statistical methods to improve algorithms and drive data-driven product decisions.
• Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders. Proven ability to present findings, insights, and strategies to senior leadership.
• Familiarity with data science tools and frameworks and an understanding of algorithm design, evaluation, and scalability.