Senior / Staff Data Scientist
Skills
About This Role
Responsibilities
- Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM).
- Develop multi-task learning approaches that optimize engagement, conversion, and merchant outcomes simultaneously.
- Build scalable retrieval and ranking systems with ANN search (FAISS, ScaNN) and vector embeddings trained on user, product, and event data.
- Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark).
- Run A/B tests and interpret results using causal inference and uplift modeling to drive measurable business impact.
- Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages.
- Define best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift).
- Partner with product analytics and data science to iterate on signal enrichment and cold-start strategies.
- Mentor junior data scientists and define best practices
Requirements
- Bachelor s or Master s degree in Computer Science, Machine Learning, or a related technical field.
- 4+ years of hands-on ML experience, including 2+ years designing or deploying large-scale recommendation systems.
- Track record: Built or maintained systems serving 1M+ users or generating 100M+ personalized predictions daily.
- Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning.
- Demonstrated success integrating multi-stage ranking systems across e-commerce surfaces (search, feeds, product detail pages) with measurable online lift (CVR, GMV).
- Proficient with large-scale data ecosystems: Kafka, Spark, ClickHouse, BigQuery, or equivalent.
- Strong understanding of offline/online evaluation metrics, A/B experimentation, and model monitoring frameworks.
- Skilled in debugging, optimization, and productionization of ML pipelines in cloud or containerized environments
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