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New York, New York, United States
Hybrid
We are hiring a Staff Product Manager to own and evolve HelloFresh's customer prediction and decisioning portfolio, the suite of ML products that forecast customer value and, increasingly, decide the next best action for every customer: the right action, message, or nudge, for the right person, on the right channel, at the right moment.
You'll lead a squad of Data Scientists and Machine Learning Engineers on a mission to build the AI-powered engine that decides how HelloFresh creates and protects customer value across millions of customers and multiple markets. In this role, you will evolve our customer prediction and decisioning portfolio from a passive measurement layer that predicts value into an active engine that prescribes and orchestrates the actions that create it, moving from generic, rules-based segmentation to a sophisticated predict-and-optimize system that pairs models of each customer's lifetime value and incremental response with optimization that spends finite budget and attention where it returns the most. Your scope spans the entire customer journey: from first discovery to reacquisition and active retention, across every owned channel.
This is a commercial decision-making role, not a single-surface personalization role. CLV is the prediction that powers it; next-best-action is what we do with it. You will own both the prediction layer and the decisioning/orchestration layer that turns predictions into action.
You will advocate for a customer-centric philosophy: optimization as empowerment, not manipulation. Working backwards from customer needs, you'll ensure our AI helps customers make better-informed decisions, with rigorous standards around transparency, privacy, and fairness. Trust is our primary growth lever when customers feel understood, it drives long-term retention, higher order frequency, and advocacy.
You will serve as the voice of the customer and the single-threaded leader across Data Science, Engineering, Commercial, Marketing, CRM, and Growth translating ML capability into customer-first experiences and aligning the organization around one decisioning strategy.
Sense the opportunity and set direction: synthesize customer insight, commercial context, competitive signal, and ML advances into a clear, prioritized multi-quarter strategy then translate it into quarterly objectives the squad can execute.
Run a predict-and-optimize portfolio: partner with Data Science to advance our CLV, propensity, and uplift models extending prediction horizons beyond the near term, moving from cohort-level to customer-level granularity, and adding action-value attribution then pair them with budget/constraint optimization so we spend each marketing dollar where it returns the most. Ensure outputs are relevant, timely, and fair across segments.
Drive adoption of predictions as decisions: deepen how prediction outputs feed experimentation guardrails, CRM/communication decisioning, and growth & product strategy so models don't just inform analysis, they drive operational action.
Scale decisioning across all markets and touch points, ensuring consistent, explainable experiences customers understand and trust while navigating complex technical and operational trade-offs.
Define success metrics that balance customer and business outcomes satisfaction, retention, and engagement on one side; conversion, customer lifetime value, incremental lift, and ROI efficiency on the other and report directly on the outcomes that matter to both customers and the business.
Prioritize ruthlessly with rigorous product practices, balancing near-term efficiency wins against long-term strategic capabilities, and making clear pragmatic calls between ML, GenAI, heuristics, and simple rules based on ROI, latency, and maintainability.
Stakeholder influence & adoption
You have a strong track record of influencing senior, cross-functional stakeholders often without direct authority and driving alignment toward clear decisions rather than just consensus. You’re able to translate complex algorithmic or technical concepts into simple, compelling business narratives for executives, and you stay resilient in large, fast-moving organizations where ambiguity is the norm.
End-to-end product craft
You bring strong product leadership across the full lifecycle from opportunity identification to vision setting, strategy, execution, and prioritization under constraint. You typically have 6+ years of product management experience at Senior or Staff level, with a track record of shipping products that have had meaningful impact at scale across multiple markets.
Data-driven fundamentals
You are highly data-native and comfortable defining success metrics, designing and interpreting A/B and incrementality experiments, and reasoning about causal impact rather than surface-level metrics. You’re comfortable working with data directly (including SQL where needed) and don’t rely solely on others to form insights or recommendations.
AI-native operator
You actively use modern AI tools as part of your daily workflow for analysis, research, prototyping, and exploration. You’re able to form and test hypotheses faster by leveraging AI, increasing both your own velocity and that of the broader team.
ML fluency & science partnership
You’re comfortable working closely with Data Science and ML Engineering teams, and can engage meaningfully in discussions around model performance, feature engineering, uplift/causal inference, and the ML lifecycle. Prior experience in personalization, recommendations, propensity models, or decisioning systems is a strong plus.
Commercial & subscription acumen
You understand subscription-based business models and how pricing, promotions, and personalized offers influence LTV, retention, and churn. You can balance commercial outcomes with customer value in your decision-making.
This job description is intended to provide a general overview of the responsibilities. However, the Company reserves the right to adjust, modify, or reassign work tasks and responsibilities as needed to meet changing business needs, operational requirements, or other factors.