Data Science Manager Salla

Responsibilities

• Own the Recommendation Roadmap: Define and execute the long-term technical and business strategy for retrieval, ranking, and personalization systems across the platform (search, home feed, and store pages).
• Model Innovation: Oversee development of state-of-the-art recommendation architectures multi-task learning, sequence-based models, and hybrid (deep + boosted) rankers.br> Drive measurable lift in engagement, CVR, and GMV.
• Lead and Grow the Pod: Manage and mentor a cross-functional team of data scientists, ML engineers, and data engineers. Foster technical excellence, iterative experimentation, and accountability for impact.
• Cross-Functional Leadership:br>Partner with Product, Infra, and Merchant Success to align modeling with business priorities. Translate ambiguous goals into clear, data-backed roadmaps.
• Scalable Deployment & Experimentation:br>Lead productionization of models with real-time feature stores (ClickHouse, Kafka, Spark), and ensure rigorous A/B testing and causal inference for model launches.
• Quality and Governance:br>Define and enforce model evaluation, monitoring, and retraining standards. Maintain consistency in feature usage, labeling, and performance reporting across surfaces.
• Collaboration with GenAI:br>Partner with the GenAI team to infuse recommendation intelligence into conversational shopping experiences and merchant assistants.
• Clear communicator capable of aligning technical and business teams around a common vision.

• Education: Bachelor s or Master s in Computer Science, Machine Learning, or a related quantitative field.
• Experience: 6+ years in applied ML, including 3+ years building or leading large-scale recommendation systems.
• Leadership: Proven experience building or growing a team of applied data scientists and delivering end-to-end projects with measurable business outcomes.
• Technical Depth: Hands-on understanding of multi-stage ranking, embeddings, and personalization architectures; able to review and guide model design.
• Experience driving online lift through A/B experimentation.br>Familiarity with data and serving infrastructure (Kafka, Spark, ClickHouse, SageMaker, etc).br> br> br>