Senior Data Scientist, Growth Alliance
HelloFresh
| Company | HelloFresh |
| Category | Data & Analytics |
| Location | Warszawa |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 9 Jul 2026 |
| Last verified | 9 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Work with HelloFresh in Warsaw and its HelloTech organisation, HelloFresh’s global technology backbone with more than 1000 people, building the digital products that power our end-to-end food experience. From meal kits and ready-to-eat meals to specialty offerings like pet food and premium meat & seafood, HelloTech creates the platforms that bring tailored food solutions to millions of customers every month.
Our subscription-based, direct-to-consumer model relies on technology at every step, from customer-facing apps and personalization logic to pricing, forecasting, supply chain optimization, and initiatives that help reduce food waste. While our brands operate independently to serve distinct customer needs, they are united by shared platforms, data, and operational excellence built by HelloTech.
HelloTech works in autonomous, cross-functional alliances, each owning a specific product or domain end to end. By working with our Warsaw office, you will help shape scalable, data-driven products used across our markets, working with a modern tech stack and international teams to continuously improve how people discover, order, and enjoy HelloFresh’s products, today and in the future.
About the role: What's in the Box
The Communications tribe, a key part of the Growth Alliance, enables our brands to deliver highly personalized, meaningful interactions with our users across global markets. As a Senior Data Scientist within this team, you will drive a direct business impact by shaping the intelligence behind how, when, and what we communicate with our customers.
Your work will primarily focus on three core initiatives:
Send Decisioning: Designing and scaling AI agents, specifically using contextual bandits, to optimize our communication channels (email, SMS, and push notifications). Your models will automate the logic that determines the most relevant message and timing for each individual user.
Intelligent Funnel: Supporting a collaborative, cross-tribe initiative aimed at personalizing the post-click experience. You will leverage contextual bandits to match user ad-context and pre-questionnaire preferences with the optimal landing page journey.
AI-Assisted Content & Search: Building intelligent systems to index and search internal marketing assets (images and copy). Additionally, you will develop tools that assist marketing teams in the automated composition of new, high-performing communication assets.
To succeed in this project, you should be a pragmatic and curious problem solver who excels in a cross-functional environment. Success involves translating complex user behavior into scalable models, identifying opportunities to improve our data foundations, and balancing technical depth with a strong focus on driving user engagement.
At HelloTech, flexibility and cross-functional collaboration are core to how we work. While this role is aligned to a specific Alliance, strong candidates may also be considered for opportunities across different teams or projects.
What you’ll do: The Recipe
Build, own, and iterate on RL and ML models that run in production behind high-traffic, performance-critical conversion and communication flows.
Develop the decisioning logic that determines the best next action for each user across the funnel and the messaging lifecycle.
Drive the architectural evolution of our contextual-bandit systems — from online exploration (Bootstrapped Thompson Sampling) toward offline policy evaluation and deterministic, low-latency inference (Counterfactual Risk Minimization, Offset Trees, model distillation).
Partner with Engineering on system design so models integrate cleanly into the funnel and messaging architecture — including where models decide, how decisions are served at latency, and how failures are handled and rolled back.
Own experimentation design for decisioning models: define success metrics and guardrails, and interpret results in high-nois