Build and iterate on machine learning models powering personalization that run in production behind high-traffic, performance-critical conversion pages.
Execute decisioning models that determine the best next step in the conversion funnel for each user.
Contribute to system design by coordinating with Engineering to ensure data science solutions integrate cleanly into the overall funnel architecture.
Coordinate directly with Product and Engineering to shape how ML-driven decisions are embedded into the conversion funnels, including where models decide and how failures are handled.
Direct experimentation design for decisioning models, defining success metrics, guardrails, and how results are interpreted in high-noise, high-traffic environments.
Maintain model safety and effectiveness in production by monitoring performance, input data reliability, and latency, and iterating when issues are detected.
What you’ll bring: The Ingredients
requirements-expected :
5+ years of professional experience in data science or a related quantitative field.
Strong foundation in machine learning concepts relevant to personalization and marketing communications.
Hands-on experience with techniques such as regression, classification, clustering, recommendation systems, and experimentation.
Practical experience designing and analyzing A/B tests and controlled experiments.
Experience working with customer behavior data and segmentation.
Extensive experience with operating in production environments and collaborating closely with engineers.
Clear, structured communication skills and a collaborative mindset.
offered :
Global collaboration at scale: Collaborate with experienced engineers and product partners across HelloTech’s international teams, in a culture of active knowledge sharing.
Technology with real-world impact: Build and operate modern systems at global scale, supporting 6+ millions of customers and complex supply chain operations.
Technical/Product/Design leadership: Drive best practices and influence architecture/design, quality, and ways of working in an autonomous, product-led setup.
End-to-end development/delivery: Drive decisions from problem definition to production, improving systems and enabling long-term scalability.
Access to workspace at Warsaw Centre Point. The hub offers modern facilities including showers, breakout zones, outdoor space, cycle parking, and refreshments (coffee, soft drinks, and fruit).
