Data Scientist

Job Title: Senior Data Scientist

Job Location: Berlin, Germany

Duration: Permanent

Work Mode: Hybrid

Languages: English

Job description:

As a Senior Data Scientist and help us accelerate how we build and deliver machine learning solutions. This is not a traditional “model development” role. We focus on scalable, end-to-end ML solutions with continuous delivery. As a senior individual contributor, you will take ownership from problem framing through production deployment and monitoring. You will help shape our matching engine and establish strong engineering and ML best practices across the full lifecycle.

Key Responsibilities

• Own ML initiatives end-to-end: from ideation and business alignment to deployment, monitoring, and iteration
• Design and build scalable solutions for apparel sizing and matching, with return rate reduction as primary KPI
• Contribute to building a generalized matching engine leveraging cross-domain data
• Implement robust data pipelines, evaluation frameworks, and real-time serving solutions (millisecond latency)
• Ship improvements continuously through structured A/B testing
• Establish and elevate ML engineering standards (code quality, review culture, reproducibility, monitoring)
• Collaborate closely with product, engineering, and stakeholders to prioritize high-impact initiatives

Requirements and Qualifications Required:

• 5+ years of experience building and shipping ML systems in production environments
• Strong experience in recommendation systems, personalization, or large-scale matching problems
• Demonstrated ownership of ML solutions from problem definition to production monitoring
• Strong software engineering mindset (clean code, testing, CI/CD, architecture awareness)
• Experience with real-time model serving under latency constraints
• Proficiency in Python and SQL
• Experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience with ML lifecycle tools (e.g., Vertex AI or similar platforms)
• Professional fluency in English (other languages is a plus but not required)

Highly Valued:

• Experience with embedding-based systems, collaborative filtering, or Bayesian modeling
• Experience building ML systems without a dedicated MLOps team
• Experience in startup or high-autonomy environments.