Senior Data Scientist (Deep Learning Ranking)

Location: Berlin, Germany; hybrid working model with face-to-face collaboration at the Berlin campus 2 days a week.

Company

hirify.global operates a local delivery platform across around 65 countries, with search functionality spanning more than 60 countries and 35 languages.

What you will do

• Design, build, and productionize deep neural ranking models, including DCN-V2, MMoE, and Two-Tower architectures, across the full lifecycle from offline experimentation to monitoring and iterative improvement.
• Use LLM coding agents such as Claude Code and Gemini to generate experiments, prototype architectures, and iterate on feature engineering and Learning to Rank workflows.
• Design overnight experiment pipelines and review hundreds of autonomous experiment runs to identify useful signals.
• Develop ranking signals, feature-store workflows, embedding-based retrieval, and production-grade model architectures.
• Monitor model drift, latency degradation, and feature pipeline integrity in high-throughput, low-latency production infrastructure.
• Collaborate with Backend Engineers, Data Engineers, and Product Managers; communicate model behavior, experiment results, and trade-offs; mentor junior and mid-level data scientists.

Requirements

• Master's degree, or Bachelor's degree with 6+ years of work experience, in Computer Science, Mathematics, Physics, or a related quantitative field.
• 4+ years of industry experience as a Data Scientist or Machine Learning Engineer applying machine learning in high-traffic production environments.
• Production-proven experience with deep learning ranking architectures, including DCN-V2, MMoE, and Two-Tower, plus multi-task learning, cross-feature interactions, and embedding optimization at scale.
• Strong foundation in Learning to Rank methods: pointwise, pairwise, and listwise; offline evaluation metrics including NDCG and MRR; and the relationship between offline metrics and online A/B outcomes.
• Proficiency with LLM coding agents and agentic CLI tools, including Claude Code or Gemini, for autonomous experiment generation, architecture prototyping, and rapid iteration.
• Familiarity with the auto-research paradigm and reviewing large volumes of autonomously generated experiment results.
• Expertise in Python and its ML ecosystem, including PyTorch, TensorFlow, and scikit-learn.
• Experience with SQL and dbt, GCP or AWS, and big-data processing with PySpark or Scala.
• Strong command of the full ML lifecycle, including feature stores, vector databases, model versioning, A/B experimentation frameworks, and high-throughput, low-latency model serving.
• Hands-on MLOps experience with CI/CD for ML pipelines, Metaflow, experiment tracking, model registries, drift detection, latency tracking, and data-quality alerting.
• Ability to navigate ambiguity, translate broad business objectives into well-scoped data science projects, and take accountability for outcomes.
• Demonstrated ability to work in cross-functional, globally distributed teams and communicate model behavior, experiment results, and trade-offs to technical and non-technical audiences.

Culture & Benefits

• Hybrid work with 2 days per week of face-to-face collaboration at the Berlin campus.
• 27 days of holiday, with an additional day in the second and third years of service.
• €1,000 educational budget, language courses, parental support, and access to Udemy Business.
• Health checkups, meditation, gym access, and life and accident insurance.
• Employee Share Purchase Plan, sabbatical bank, public transportation ticket discount, and corporate pension plan.
• Digital meal vouchers and food vouchers; relocation support and information for moving to Berlin.

Hiring process

• Prepare for interviews using the employer's interview guidance and common interview questions.
• Accessibility accommodations and specific interview support can be requested during the process.