Senior Data Scientist – MarTech (Measurement & Optimization)

Glovo is part of the Delivery Hero Group, the world’s pioneering local delivery platform, our mission is to deliver an amazing experience—fast, easy, and to your door. We operate in around 65 countries worldwide. Headquartered in Berlin, Germany. Delivery Hero has been listed on the Frankfurt Stock Exchange since 2017 and is part of the MDAX stock market index.

The MarTech Data Science team drives performance marketing efficiency through advanced measurement, optimization, and scalable modeling.

We operate in a highly complex and ambiguous environment where:

• Ground truth is often unobservable (incrementality vs attribution)

  
  
  

• Clean experimentation is not always feasible

  
  
  

• Decisions must be made under uncertainty with imperfect data

  
  
  

We build solutions for budget allocation, incrementality measurement, and bidding, enabling the business to invest each euro where it generates the highest impact. 

YOUR MISSION

We are looking for aSenior Data Scientistto own end-to-end initiatives, shape problem definitions in ambiguous contexts, and drive decision-making through rigorous models. Operating at a global level, you will design solutions that serve the entire Delivery Hero portfolio, including brands like Glovo, Talabat, and PedidosYa. This role requires strong causal inference and modeling expertise, combined with the ability to influence stakeholders and translate complex marketing problems into robust, production-grade solutions.

THE JOURNEY

Own end-to-end marketing measurement & optimization solutions

• Lead ambiguous, high-impact problem spaces from problem framing to production

  
  
  

• Define success metrics and modeling approaches when requirements are not clearly specified

  
  
  

• Balance methodological rigor with business constraints and timelines

  
  
  

Translate ambiguity into decision frameworks

• Convert vague marketing questions into structured, model-driven decision systems

  
  
  

• Operate in environments where experimentation is limited or infeasible, and triangulate between MMM, experiments, and observational methods

  
  
  

• Make and justify assumptions explicitly, and assess their impact on decisions

  
  
  

Drive decision-making under uncertainty

• Provide clear recommendations despite imperfect measurement, articulating trade-offs and confidence levels

  
  
  

• Navigate conflicting signals (e.g., attribution vs incrementality vs MMM)

  
  
  

• Ensure outputs are actionable and aligned with real business constraints (budget caps, pacing, channel dependencies)

  
  
  

Lead methodological design, validation, knowledge transfer

• Design and evolve frameworks across MMM, incrementality testing (geo experiments, synthetic control), bidding, and LTV

  
  
  

• Establish robust validation strategies in the absence of ground truth (cross-method validation, backtesting, sensitivity analysis)

  
  
  

• Set standards for statistical rigor, interpretability, and reproducibility

  
  
  

• Drive internal & external knowledge sharing across Delivery Hero and industry.

  
  
  

Drive adoption and stakeholder alignment

• Translate complex modeling outputs into clear narratives for non-technical stakeholders

  
  
  

• Communicate with stakeholders ranging from squad ICs to tribe leadership, adapting abstraction level appropriately

  
  
  

• Handle pushback on model outputs and build trust in methodologies over time

  
  
  

Build production-grade systems

• Develop reliable, maintainable solutions with high standards in testing, monitoring, documentation, and reproducibility

  
  
  

• Work closely with Engineering and Product to deploy, scale, and continuously improve systems

  
  
  

• Ensure long-term usability of models as decision products, not just analyses

  
  
  

Statistical, Causal & Predictive Expertise

• Strong foundation in experimentation, causal inference, time-series, and predictive modeling

  
  
  

• Experience applying these methods in imperfect real-world settings with noisy or biased data

  
  
  

Decision-Making in Ambiguous Environments

• Operate without clearly defined problem statements

  
  
  

• Make sound recommendations under uncertainty

  
  
  

• Balance conflicting methodologies and imperfect signals

  
  
  

End-to-End Ownership

• Track record of owning problems from open-ended business question → modeling approach → production system → business adoption

  
  
  

MarTech Domain Expertise

• Deep understanding of attribution, digital and offline channels, performance marketing, bidding, and budget allocation.

  
  
  

• Experience dealing with incrementality, diminishing returns, and cross-channel interactions in practice.

  
  
  

Stakeholder Influence & Communication

• Communicate complex statistical concepts to non-technical stakeholders

  
  
  

• Drive adoption of model-based decisions in environments with competing narratives

  
  
  

• Build trust in data products over time

  
  
  

Engineering & Production Excellence

• Strong Python and SQL skills (e.g., BigQuery)

  
  
  

• Experience with production-grade practices: modular design, testing, CI/CD, version control, modern data/ML stacks.

  
  
  

• Experience leveraging agentic workflows (LLM-driven or tool-using agents) to scale data science tasks.

  
  
  

Pragmatic Innovation

• Up-to-date with methods such as uplift modeling, MMM, quasi-experiments, and survival models

  
  
  

• Ability to translate advanced methods into scalable systems that deliver real business impact, not just theoretical improvements