Head of Data Science – AI

Marley Spoon

Head of Data Science & AI leading a team to enhance strategies using data in a global food-tech company. Focus on customer experience, forecasting, and waste reduction through machine learning.

Posted 7/29/2026full-timeLisbon • 🇵🇹 PortugalLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Science and Machine Learning, with a strong focus on leading teams, developing scalable models, and implementing MLOps practices. Proficient in translating complex technical concepts into actionable business strategies while championing responsible AI and data literacy.

Highest-signal resume keywords

Data Science LeadershipMachine Learning ExpertisePython ProficiencyMLOps PracticesA/B Testing Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModellingNatural Language ProcessingTime-Series ForecastingRecommender SystemsPythonSQLScikit-LearnXGBoostPyTorch

Soft Skills

Team BuildingMentoringBusiness JudgmentCommunicationCollaboration

Tools & Technologies

SnowflakeLookerMLOps PlatformsOrchestration Platforms

Industry Keywords

E-CommerceFood-TechConsumer-ProductPersonalizationCustomer Lifecycle ModellingOperational Forecasting

Tech Stack

Tools & technologies

PythonPyTorchScikit-LearnSQL

About the role

Key responsibilities & impact

Define the long-term strategy and roadmap for Data Science, Machine Learning and AI, aligning investment and delivery with company priorities.
Build, lead and mentor a high-performing team of Data Scientists and ML Engineers.
Guide the development of recommendation and personalization models that tailor meals, recipes and content to customer preferences and behaviour.
Lead forecasting and planning initiatives that improve demand prediction, resource allocation, logistics decisions and food waste reduction.
Strengthen retention and marketing decision-making through churn prediction, customer lifetime value modelling, reactivation, segmentation, campaign optimization, ROI and attribution models.
Embed experimentation into how we work through robust A/B testing and multivariate analysis.
Evaluate and introduce large language models, generative AI and other emerging approaches when they provide practical customer or business value.
Oversee the complete machine learning lifecycle—from development and validation to deployment, monitoring and retraining—using MLOps practices that support reliability, scalability and governance.
Partner with Product, Engineering, Marketing and Operations to identify high-value opportunities and translate technical work into clear business outcomes.
Select and manage external platforms, tools and partners supporting our machine learning infrastructure and Data Science capabilities.
Champion responsible AI and strengthen data and AI literacy across the organization.

Requirements

What you’ll need

8+ years of experience in Data Science, including at least 3 years leading teams and delivering production machine learning systems.
Strong expertise in machine learning, statistical modelling, natural language processing, time-series forecasting and recommender systems.
Strong proficiency in Python and SQL, with experience using frameworks such as scikit-learn, XGBoost and PyTorch.
Experience developing and operating scalable production models using orchestration platforms and MLOps practices.
Experience with modern data and analytics platforms such as Snowflake and Looker, or comparable technologies.
Strong knowledge of experimentation, including A/B testing and multivariate testing.
Proven experience building, mentoring and developing high-performing teams of Data Scientists and ML Engineers.
The ability to translate complex technical concepts into clear recommendations for senior and non-technical stakeholders.
Strong business judgment and a record of connecting technical initiatives to measurable outcomes.
Experience working in Agile, cross-functional product and engineering environments.
A pragmatic approach to responsible AI, model governance and emerging technologies, including large language models and generative AI.
Experience in subscription, e-commerce, food-tech or consumer-product environments would be valuable, particularly where personalization, customer lifecycle modelling or operational forecasting played an important role.

Benefits

Comp & perks

Hybrid work policy (remote + office).
22 annual leave days +2 days extra days for every year of tenure (up to 6).
5 training days per year.
Private health insurance provided by Tranquilidade.
Food allowance of 7.62€/worked day under by Coverflex.
24/7 confidential employee assistance program.